2026
Conference article
Open Access
Remind me of something? Zero-Shot learning for trustworthy image comparison in rolling stock
Papini Oscar, Del Corso Giulio, Bulotta Davide, Carboni Andrea, Gravili Silvia, Leone Giuseppe Riccardo, Pascali Maria Antonietta, Moroni Davide, Colantonio Sara
This paper discusses the need for trustworthy AI in urban mobility, focusing on high-stakes security applications such as anomaly detection in public transportation. Because the accuracy required to identify potentially dangerous objects often surpasses the capabilities of current models, there is an unavoidable incidence of false positives. We suggest a "learning to defer" approach as a solution. Our technique uses the deep features and label relative importance of a pre-trained classifier (DenseNet/ImageNET-1k) to create a unique item "fingerprint". We then employ a zero-shot meta-learning approach to calibrate the system, enabling it to distinguish between normal background items and genuine anomalies by assigning a similarity score. This method significantly reduces the false "new object" alarms that would otherwise overwhelm human operators. Our proof-of-concept demonstrates that the system is computationally light and can be easily adapted to specific environments and integrated into existing classification modules.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16170, pp. 323-334. Roma, Italy, 15-19/09/2025
DOI: 10.1007/978-3-032-11381-8_28
Project(s): FAITH
Metrics:
Papini Oscar, Del Corso Giulio, Bulotta Davide, Carboni Andrea, Gravili Silvia, Leone Giuseppe Riccardo, Pascali Maria Antonietta, Moroni Davide, Colantonio Sara
This paper discusses the need for trustworthy AI in urban mobility, focusing on high-stakes security applications such as anomaly detection in public transportation. Because the accuracy required to identify potentially dangerous objects often surpasses the capabilities of current models, there is an unavoidable incidence of false positives. We suggest a "learning to defer" approach as a solution. Our technique uses the deep features and label relative importance of a pre-trained classifier (DenseNet/ImageNET-1k) to create a unique item "fingerprint". We then employ a zero-shot meta-learning approach to calibrate the system, enabling it to distinguish between normal background items and genuine anomalies by assigning a similarity score. This method significantly reduces the false "new object" alarms that would otherwise overwhelm human operators. Our proof-of-concept demonstrates that the system is computationally light and can be easily adapted to specific environments and integrated into existing classification modules.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16170, pp. 323-334. Roma, Italy, 15-19/09/2025
DOI: 10.1007/978-3-032-11381-8_28
Project(s): FAITH
Metrics:
See at:
CNR IRIS 
| link.springer.com | CNR IRIS 
| CNR IRIS
2025
Other
Open Access
SI-Lab Annual Research Report 2024
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
2025
Journal article
Restricted
NAVIGATOR: a regional multimodal imaging biobank initiative powered by AI tools for precision medicine in oncology
Aghakhanyan G., Barucci A., Pascali M. A., Assante M., Bagnacci G., Bertelli E., Caputo F. P., Cuibari M. E., Carlini E., Carpi R., Caudai C., Cioni D., Colantonio S., Colcelli V., Dell'Amico A., Vecchio V. D., Gangi D. D., Faggioni L., Formica V., Francischello R., Frosini L., Kotsa C., Lipari G., Manghi P., Martino V. D., Marzi C., Mazzei M. A., Mangiacrapa F., Meglio N. D., Miele V., Molinaro E., Paiar F., Pagano P., Panichi G., Pasquinelli F., Peccerillo B., Perrella A., Piccioli T., Oliviero A., Olivoni M., Rucci D., Tampucci M., Tumminello L., Volpini F., Zanuzzi A., Fanni S. C., Neri E.
The NAVIGATOR project established an Italian regional imaging biobank and interactive research platform designed to support precision oncology through the integration of multimodal imaging, clinical, and omics data. The platform goes beyond a static repository, offering a secure Virtual Research Environment (VRE) where users can upload data, test AI algorithms, and execute complete analytical pipelines. The platform incorporates artificial intelligence (AI)-driven radiomics and deep learning methodologies to enable biomarker extraction, disease stratification, and predictive modeling. This manuscript presents the development and implementation of the NAVIGATOR infrastructure, including its data governance framework, ethical and legal considerations, and application to three oncological use cases: prostate, rectal, and gastric cancers. To date, the biobank has collected imaging and clinical data from over 700 patients across these cohorts. AI models were deployed within a dedicated VRE to facilitate image analysis, feature extraction, and classification tasks. The project addresses critical challenges related to data harmonization, regulatory compliance, privacy safeguards and fairness in AI systems. NAVIGATOR demonstrates the feasibility of integrating AI methodologies within imaging biobanks and provides a scalable framework to advance oncological research and support clinical decision-making.Source: EUROPEAN JOURNAL OF RADIOLOGY, vol. 191 (issue 112327)
DOI: 10.1016/j.ejrad.2025.112327
Project(s): An Imaging Biobank to Precisely Prevent and Predict cancer, and facilitate the Participation of oncologic patients to Diagnosis and Treatment
Metrics:
Aghakhanyan G., Barucci A., Pascali M. A., Assante M., Bagnacci G., Bertelli E., Caputo F. P., Cuibari M. E., Carlini E., Carpi R., Caudai C., Cioni D., Colantonio S., Colcelli V., Dell'Amico A., Vecchio V. D., Gangi D. D., Faggioni L., Formica V., Francischello R., Frosini L., Kotsa C., Lipari G., Manghi P., Martino V. D., Marzi C., Mazzei M. A., Mangiacrapa F., Meglio N. D., Miele V., Molinaro E., Paiar F., Pagano P., Panichi G., Pasquinelli F., Peccerillo B., Perrella A., Piccioli T., Oliviero A., Olivoni M., Rucci D., Tampucci M., Tumminello L., Volpini F., Zanuzzi A., Fanni S. C., Neri E.
The NAVIGATOR project established an Italian regional imaging biobank and interactive research platform designed to support precision oncology through the integration of multimodal imaging, clinical, and omics data. The platform goes beyond a static repository, offering a secure Virtual Research Environment (VRE) where users can upload data, test AI algorithms, and execute complete analytical pipelines. The platform incorporates artificial intelligence (AI)-driven radiomics and deep learning methodologies to enable biomarker extraction, disease stratification, and predictive modeling. This manuscript presents the development and implementation of the NAVIGATOR infrastructure, including its data governance framework, ethical and legal considerations, and application to three oncological use cases: prostate, rectal, and gastric cancers. To date, the biobank has collected imaging and clinical data from over 700 patients across these cohorts. AI models were deployed within a dedicated VRE to facilitate image analysis, feature extraction, and classification tasks. The project addresses critical challenges related to data harmonization, regulatory compliance, privacy safeguards and fairness in AI systems. NAVIGATOR demonstrates the feasibility of integrating AI methodologies within imaging biobanks and provides a scalable framework to advance oncological research and support clinical decision-making.Source: EUROPEAN JOURNAL OF RADIOLOGY, vol. 191 (issue 112327)
DOI: 10.1016/j.ejrad.2025.112327
Project(s): An Imaging Biobank to Precisely Prevent and Predict cancer, and facilitate the Participation of oncologic patients to Diagnosis and Treatment
Metrics:
See at:
European Journal of Radiology | Archivio della Ricerca - Università di Pisa | CNR IRIS | CNR IRIS | Archivio della Ricerca - Università di Pisa
2025
Contribution to book
Open Access
AI models in cancer diagnosis and prognosis
Filos D., Chouvarda I., Sykiotis S., Tzortzis I., Rallis I., Doulamis A., Doulamis N., De Almeida J. G., Rodrigues N., Rodrigues A. C., Papanikolaou N., Loncar-Turukalo T., Jakovljevic N., Lazic I., Rapaic M., Hautaniemi S., Caudai C., Del Corso G., Germanese D., Pachetti E., Pascali M. A., Colantonio S.
The increasing volume of collected cancer imaging data, together with the development of technological tools based on Artificial Intelligence (AI), offers unprecedented opportunities for enhancing cancer management and improving clinical workflows. This chapter explores the approaches adopted by two projects within the AI for Health Imaging (AI4HI) cluster, INCISIVE and ProCancer-I, targeting various cancer types. In total, sixteen models were implemented across two projects, focusing on prostate, breast, lung, and ovarian cancers. These models were designed for lesion segmentation, patient stratification, and predicting metastasis risk or radiotherapy side effects, utilizing various DL and ML architectures such as YOLO, ResUnet++, and U-Net. Diverse imaging modalities were used, including Magnetic Resonance Imaging, Computed Tomography, and Mammography, while whole-slide images were used for the detection and classification of cell types in histopathological images. Radiomics was employed for classification and prediction by extracting features from imaging data, with harmonization techniques applied to improve model generalizability. Although some models incorporated clinical data, most relied on imaging features, highlighting the potential for improved performance by integrating multimodal data. To further enhance model performance and generalizability, comprehensive repositories with detailed clinical and follow-up data are needed. Additionally, addressing model fairness, explainability, and biological validation is essential for gaining acceptance within the clinical community.DOI: 10.1007/978-3-031-89963-8_7
Project(s): An AI Platform integrating imaging data and models, supporting precision care through prostate cancer’s continuum
Metrics:
Filos D., Chouvarda I., Sykiotis S., Tzortzis I., Rallis I., Doulamis A., Doulamis N., De Almeida J. G., Rodrigues N., Rodrigues A. C., Papanikolaou N., Loncar-Turukalo T., Jakovljevic N., Lazic I., Rapaic M., Hautaniemi S., Caudai C., Del Corso G., Germanese D., Pachetti E., Pascali M. A., Colantonio S.
The increasing volume of collected cancer imaging data, together with the development of technological tools based on Artificial Intelligence (AI), offers unprecedented opportunities for enhancing cancer management and improving clinical workflows. This chapter explores the approaches adopted by two projects within the AI for Health Imaging (AI4HI) cluster, INCISIVE and ProCancer-I, targeting various cancer types. In total, sixteen models were implemented across two projects, focusing on prostate, breast, lung, and ovarian cancers. These models were designed for lesion segmentation, patient stratification, and predicting metastasis risk or radiotherapy side effects, utilizing various DL and ML architectures such as YOLO, ResUnet++, and U-Net. Diverse imaging modalities were used, including Magnetic Resonance Imaging, Computed Tomography, and Mammography, while whole-slide images were used for the detection and classification of cell types in histopathological images. Radiomics was employed for classification and prediction by extracting features from imaging data, with harmonization techniques applied to improve model generalizability. Although some models incorporated clinical data, most relied on imaging features, highlighting the potential for improved performance by integrating multimodal data. To further enhance model performance and generalizability, comprehensive repositories with detailed clinical and follow-up data are needed. Additionally, addressing model fairness, explainability, and biological validation is essential for gaining acceptance within the clinical community.DOI: 10.1007/978-3-031-89963-8_7
Project(s): An AI Platform integrating imaging data and models, supporting precision care through prostate cancer’s continuum
Metrics:
See at:
CNR IRIS | link.springer.com | doi.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector
Gaivão A. M., Bireiro C., Santiago I., Joana Ip, Belião S., Matos C., Vanneschi L., Tsiknakis M., Marias K., Regge D., Silva S., Sfakianakis S., Kalokyri V., Trivizakis E., Kalliatakis G., Dimitriadis A., Fotiadis D., Tachos N., Mylona E., Zaridis D., Kalantzopoulos C., Papanikolaou N., De Almeida J. G., Castro Verde A., Rodrigues A. C., Rodrigues N., Chambel M., Huisman H., De Rooij M., Saha A., Twilt J. J., Futterer J., Martí-Bonmatí L., Cerdá-Alberich L., Ribas G., Navarro S., Marfil M., Neri E., Aringhieri G., Tumminello L., Mendola M., Akata V., Özmen M., Karaosmanoglu A. D., Atak F., Karcaaltincaba M., Vilanova J. C., Usinskiene J., Briediene R., Untanas A., Slidevska K., Vasilis K., Georgios G., Koh D. -M., Emsley R., Vit S., Ribeiro A., Doran S., Jacobs T., García-Martí G., Giannini V., Mazzetti S., Cappello G., Maimone G., Napolitano V., Colantonio S., Pascali M. A., Pachetti E., Del Corso G., Germanese D., Berti A., Carloni G., Kalpathy-Cramer J., Bridge C., Correia J., Hernandez W., Giavri Z., Pollalis C., Agraniotis D., Jiménez Pastor A., Munuera Mora J., Saillant C., Henne T., Marquez R.
Despite being one of the most prevalent cancers, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Currently, several screening and diagnostic tests are required to be carried out in order to detect PCa. These tests are often invasive, requiring either a biopsy (Gleason score and ISUP) or blood tests (PSA). Computational methods have been shown to help this process, using multiparametric MRI (mpMRI) data to detect PCa, effectively providing value during the diagnosis and monitoring stages. While delineating lesions requires a high degree of experience and expertise from the radiologists, being subject to a high degree of inter-observer variability, often leading to inconsistent readings, these computational models can leverage the information from mpMRI to locate the lesions with a high degree of certainty. By considering as positive samples only those that have an ISUP2 we can train aggressive index lesion detection models. The main advantage of this approach is that, by focusing only on aggressive disease, the output of such a model can also be seen as an indication for biopsy, effectively reducing unnecessary biopsy screenings. In this work, we utilize both the highly heterogeneous ProstateNet dataset, and the PI-CAI dataset, to develop accurate aggressive disease detection models.Source: SCIENTIFIC REPORTS, vol. 15 (issue 1)
DOI: 10.1038/s41598-025-99795-y
Project(s): ProCAncer-I
Metrics:
Gaivão A. M., Bireiro C., Santiago I., Joana Ip, Belião S., Matos C., Vanneschi L., Tsiknakis M., Marias K., Regge D., Silva S., Sfakianakis S., Kalokyri V., Trivizakis E., Kalliatakis G., Dimitriadis A., Fotiadis D., Tachos N., Mylona E., Zaridis D., Kalantzopoulos C., Papanikolaou N., De Almeida J. G., Castro Verde A., Rodrigues A. C., Rodrigues N., Chambel M., Huisman H., De Rooij M., Saha A., Twilt J. J., Futterer J., Martí-Bonmatí L., Cerdá-Alberich L., Ribas G., Navarro S., Marfil M., Neri E., Aringhieri G., Tumminello L., Mendola M., Akata V., Özmen M., Karaosmanoglu A. D., Atak F., Karcaaltincaba M., Vilanova J. C., Usinskiene J., Briediene R., Untanas A., Slidevska K., Vasilis K., Georgios G., Koh D. -M., Emsley R., Vit S., Ribeiro A., Doran S., Jacobs T., García-Martí G., Giannini V., Mazzetti S., Cappello G., Maimone G., Napolitano V., Colantonio S., Pascali M. A., Pachetti E., Del Corso G., Germanese D., Berti A., Carloni G., Kalpathy-Cramer J., Bridge C., Correia J., Hernandez W., Giavri Z., Pollalis C., Agraniotis D., Jiménez Pastor A., Munuera Mora J., Saillant C., Henne T., Marquez R.
Despite being one of the most prevalent cancers, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Currently, several screening and diagnostic tests are required to be carried out in order to detect PCa. These tests are often invasive, requiring either a biopsy (Gleason score and ISUP) or blood tests (PSA). Computational methods have been shown to help this process, using multiparametric MRI (mpMRI) data to detect PCa, effectively providing value during the diagnosis and monitoring stages. While delineating lesions requires a high degree of experience and expertise from the radiologists, being subject to a high degree of inter-observer variability, often leading to inconsistent readings, these computational models can leverage the information from mpMRI to locate the lesions with a high degree of certainty. By considering as positive samples only those that have an ISUP2 we can train aggressive index lesion detection models. The main advantage of this approach is that, by focusing only on aggressive disease, the output of such a model can also be seen as an indication for biopsy, effectively reducing unnecessary biopsy screenings. In this work, we utilize both the highly heterogeneous ProstateNet dataset, and the PI-CAI dataset, to develop accurate aggressive disease detection models.Source: SCIENTIFIC REPORTS, vol. 15 (issue 1)
DOI: 10.1038/s41598-025-99795-y
Project(s): ProCAncer-I
Metrics:
See at:
doaj.org | doi.org | CNR IRIS | www.nature.com | Archivio Istituzionale della Ricerca (AperTO) - Università di Torino | Archivio Istituzionale della Ricerca (AperTO) - Università di Torino | CNR IRIS | pubmed.ncbi.nlm.nih.gov
2025
Other
Open Access
ISTI-day 2025 Proceedings
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
See at:
CNR IRIS | www.isti.cnr.it | CNR IRIS
2025
Conference article
Open Access
Towards trustworthy AI in the public transport domain
Leone G. R., Carboni A., Del Corso G., Gravili S., Moroni D., Pascali M. A., Colantonio S.
In the context of rapidly evolving urban landscapes, the demand for enhanced mobility services has become increasingly critical. Traditional transportation systems struggle to keep pace with the growing complexity of commuting patterns and the diverse needs of urban residents. While AI can play a strong role in addressing these emerging demands, a parallel need for trustworthy services is also arising, which must be adequately met to ultimately provide equitable and ethical services to society. Based on these considerations, we explore the relevant dimensions of AI trustworthiness and propose how they can be transferred and demonstrated in a large-scale pilot focused on public transportation and exploiting advanced visual analytics paradigms based on pervasive computing. To this end, we present the FAITH risk management framework, ongoing activities, and preliminary results towards its implementation in the pilot project.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4121. Trieste, Italy, 23-24/06/2025
Leone G. R., Carboni A., Del Corso G., Gravili S., Moroni D., Pascali M. A., Colantonio S.
In the context of rapidly evolving urban landscapes, the demand for enhanced mobility services has become increasingly critical. Traditional transportation systems struggle to keep pace with the growing complexity of commuting patterns and the diverse needs of urban residents. While AI can play a strong role in addressing these emerging demands, a parallel need for trustworthy services is also arising, which must be adequately met to ultimately provide equitable and ethical services to society. Based on these considerations, we explore the relevant dimensions of AI trustworthiness and propose how they can be transferred and demonstrated in a large-scale pilot focused on public transportation and exploiting advanced visual analytics paradigms based on pervasive computing. To this end, we present the FAITH risk management framework, ongoing activities, and preliminary results towards its implementation in the pilot project.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4121. Trieste, Italy, 23-24/06/2025
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Topological machine learning for discriminative spectral band identification in raman spectroscopy of pathological samples
Conti F., Moroni D., Pascali M. A.
In the field of Raman spectroscopy (RS), particularly when working with biological samples, identifying the chemical compounds most involved in specific pathologies is of critical importance for pathologists. The correlation between chemical substances present in biological tissue and pathology can contribute not only to a deeper understanding of the disease itself but also to the development of novel artificial intelligence-based diagnostic methodologies. Motivated by these clinical challenges, we propose a method to identify the most discriminative spectral bands by leveraging the synergy between Topological Machine Learning (TML) and Raman spectroscopy. The intrinsic explainability of part of the TML pipeline can indeed play a key role in the detection of such spectral bands, e.g., the proteins most associated with the disease. In order to evaluate the performance of our method, we apply it to three case studies: the RS of biological tissue related to the chondrogenic bone tumors, the RS of cerebrospinal fluid associated with Alzheimer’s disease and the RS of pancreatic tissue. The results obtained with our method are promising in pinpointing which spectral bands are most relevant for diagnosis, but they also highlight the need for further investigation.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129053
Metrics:
Conti F., Moroni D., Pascali M. A.
In the field of Raman spectroscopy (RS), particularly when working with biological samples, identifying the chemical compounds most involved in specific pathologies is of critical importance for pathologists. The correlation between chemical substances present in biological tissue and pathology can contribute not only to a deeper understanding of the disease itself but also to the development of novel artificial intelligence-based diagnostic methodologies. Motivated by these clinical challenges, we propose a method to identify the most discriminative spectral bands by leveraging the synergy between Topological Machine Learning (TML) and Raman spectroscopy. The intrinsic explainability of part of the TML pipeline can indeed play a key role in the detection of such spectral bands, e.g., the proteins most associated with the disease. In order to evaluate the performance of our method, we apply it to three case studies: the RS of biological tissue related to the chondrogenic bone tumors, the RS of cerebrospinal fluid associated with Alzheimer’s disease and the RS of pancreatic tissue. The results obtained with our method are promising in pinpointing which spectral bands are most relevant for diagnosis, but they also highlight the need for further investigation.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129053
Metrics:
2025
Conference article
Open Access
Leveraging AI for Signal and Image Analysis in Medicine and Health
Marco Cafiso, Andrea Carboni, Claudia Caudai, Sara Colantonio, Francesco Conti, Mario D’acunto, Said Daoudagh, Giulio Del Corso, Danila Germanese, Giacomo Ignesti, Gianmarco Lazzini, Giuseppe Riccardo Leone, Massimo Magrini, Davide Moroni, Francesca Pardini, Maria Antonietta Pascali, Paolo Paradisi, Federico Volpini
The integration of artificial intelligence (AI) into the medical domain is driving innovation and progress in healthcare. This paper summarizes the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in several applications, e.g., in the analysis of biomedical data, and in the development of tools for enhancing trustworthiness and reliability of AI based systems. From cancer diagnosis and grading, to the analysis of body physiological signals to improve the understanding of dance movement therapy as an approach to healthy aging, this work highlights the paradigm shift that AI has brought into medicine and healthcare.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4121. Trieste, June 23-24, 2025
Marco Cafiso, Andrea Carboni, Claudia Caudai, Sara Colantonio, Francesco Conti, Mario D’acunto, Said Daoudagh, Giulio Del Corso, Danila Germanese, Giacomo Ignesti, Gianmarco Lazzini, Giuseppe Riccardo Leone, Massimo Magrini, Davide Moroni, Francesca Pardini, Maria Antonietta Pascali, Paolo Paradisi, Federico Volpini
The integration of artificial intelligence (AI) into the medical domain is driving innovation and progress in healthcare. This paper summarizes the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in several applications, e.g., in the analysis of biomedical data, and in the development of tools for enhancing trustworthiness and reliability of AI based systems. From cancer diagnosis and grading, to the analysis of body physiological signals to improve the understanding of dance movement therapy as an approach to healthy aging, this work highlights the paradigm shift that AI has brought into medicine and healthcare.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4121. Trieste, June 23-24, 2025
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2025
Conference article
Restricted
Image mining: current problems in theory and applications
Gurevich I., Moroni D., Pascali M. A., Radig B., Yashina V.
Image mining is the most promising and complex scientific direction of image analysis, dedicated to extracting knowledge and information from images, necessary for interpreting and understanding images and making intelligent decisions regarding objects, processes, events and phenomena presented in the image. Image mining is based on the methods of the mathematical theory of image analysis, the mathematical theory of pattern recognition and mathematical linguistics. Automation of image mining is one of the most important strategic goals in image analysis, recognition and understanding both in scientific and technological aspects. The main subgoals are developing and applying of mathematical theory for constructing image models and representations allowable by efficient pattern recognition algorithms and for constructing standardized representations and selection of image analysis transforms. Our analysis showed that the main directions of current fundamental and applied research in the field of image mining are the following: research of the image formalization space;development, research and application of mathematical and heuristic methods for constructing formal models and representations of images;development, research and application of mathematical and heuristic methods for constructing transformations of formal models and representations of images;study of the information nature of the image;study of the image as a new class of mathematical objects. research of the image formalization space; development, research and application of mathematical and heuristic methods for constructing formal models and representations of images; development, research and application of mathematical and heuristic methods for constructing transformations of formal models and representations of images; study of the information nature of the image; study of the image as a new class of mathematical objects. The publication presents an introductory paper to the IMTA Proceedings. The main scientific results of the 9th International Workshop “Image Mining: Theory and Applications,” held on December 1, 2024, Kolkata, India, are presented.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15616, pp. 49-62. Kolkata, India, 01/12/2024
DOI: 10.1007/978-3-031-87663-9_4
Metrics:
Gurevich I., Moroni D., Pascali M. A., Radig B., Yashina V.
Image mining is the most promising and complex scientific direction of image analysis, dedicated to extracting knowledge and information from images, necessary for interpreting and understanding images and making intelligent decisions regarding objects, processes, events and phenomena presented in the image. Image mining is based on the methods of the mathematical theory of image analysis, the mathematical theory of pattern recognition and mathematical linguistics. Automation of image mining is one of the most important strategic goals in image analysis, recognition and understanding both in scientific and technological aspects. The main subgoals are developing and applying of mathematical theory for constructing image models and representations allowable by efficient pattern recognition algorithms and for constructing standardized representations and selection of image analysis transforms. Our analysis showed that the main directions of current fundamental and applied research in the field of image mining are the following: research of the image formalization space;development, research and application of mathematical and heuristic methods for constructing formal models and representations of images;development, research and application of mathematical and heuristic methods for constructing transformations of formal models and representations of images;study of the information nature of the image;study of the image as a new class of mathematical objects. research of the image formalization space; development, research and application of mathematical and heuristic methods for constructing formal models and representations of images; development, research and application of mathematical and heuristic methods for constructing transformations of formal models and representations of images; study of the information nature of the image; study of the image as a new class of mathematical objects. The publication presents an introductory paper to the IMTA Proceedings. The main scientific results of the 9th International Workshop “Image Mining: Theory and Applications,” held on December 1, 2024, Kolkata, India, are presented.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15616, pp. 49-62. Kolkata, India, 01/12/2024
DOI: 10.1007/978-3-031-87663-9_4
Metrics:
2025
Conference article
Open Access
Topological machine learning for Raman spectroscopy: perspectives for pancreatic diseases
Conti F., Lazzini G., Gaeta R., Pollina L. E., Comandatore A., Furbetta N., Morelli L., D'Acunto M., Moroni D., Pascali M. A.
The analysis of tissue samples from 17 subjects clinically diagnosed with chronic pancreatitis, ductal adenocarcinoma, or classified as controls has been collected and analyzed by Raman spectroscopy (RS). Such data are classified using a recent methodology which combines machine learning with advanced topological data analysis (TDA) techniques, known as topological machine learning (TML). A classification accuracy of 82% was achieved following a cross-validation scheme with patient stratification, suggesting that the combination of RS and topological data analysis holds significant potential for distinguishing between the three diagnostic categories. When restricted to binary classification (cancer vs. no cancer), performance increases to 88%. This approach offers a promising and fast method to support clinical diagnoses, potentially improving diagnostic accuracy and patient outcomes.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129061
Metrics:
Conti F., Lazzini G., Gaeta R., Pollina L. E., Comandatore A., Furbetta N., Morelli L., D'Acunto M., Moroni D., Pascali M. A.
The analysis of tissue samples from 17 subjects clinically diagnosed with chronic pancreatitis, ductal adenocarcinoma, or classified as controls has been collected and analyzed by Raman spectroscopy (RS). Such data are classified using a recent methodology which combines machine learning with advanced topological data analysis (TDA) techniques, known as topological machine learning (TML). A classification accuracy of 82% was achieved following a cross-validation scheme with patient stratification, suggesting that the combination of RS and topological data analysis holds significant potential for distinguishing between the three diagnostic categories. When restricted to binary classification (cancer vs. no cancer), performance increases to 88%. This approach offers a promising and fast method to support clinical diagnoses, potentially improving diagnostic accuracy and patient outcomes.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129061
Metrics:
See at:
CNR IRIS | www.mdpi.com | CNR IRIS
2024
Conference article
Open Access
Advancing sustainability: research initiatives at the Signals and Images Lab
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Harnessing topological machine learning in Raman spectroscopy: perspectives for Alzheimer’s disease detection via cerebrospinal fluid analysis
Conti F., Banchelli M., Bessi V., Cecchi C., Chiti F., Colantonio S., D'Andrea C., De Angelis M., Moroni D., Nacmias B., Pascali M. A., Sorbi S., Matteini P.
The cerebrospinal fluid of 21 subjects who received a clinical diagnosis of Alzheimer’s disease (AD) as well as of 22 pathological controls has been collected and analysed by Raman spectroscopy (RS). We investigated whether the Raman spectra could be used to distinguish AD from controls, after a preprocessing procedure. We applied machine learning to a set of topological descriptors extracted from the spectra, achieving a high classification accuracy of 86%. Our experimentation indicates that RS and topological analysis may be a reliable and effective combination to confirm or disprove a clinical diagnosis of Alzheimer’s disease. The following steps will aim at leveraging the intrinsic interpretability of the topological data analysis to characterize the AD subtypes, e.g. by identifying the bands of the Raman spectrum relevant for AD detection, possibly increasing and/or confirming the knowledge about the precise molecular events and biological pathways behind the Alzheimer’s disease.Source: JOURNAL OF THE FRANKLIN INSTITUTE, vol. 361 (issue 18)
DOI: 10.1016/j.jfranklin.2024.107249
Project(s): Proteomics, RAdiomics & Machine learning-integrated strategy for precision medicine for Alzheimer’s
Metrics:
Conti F., Banchelli M., Bessi V., Cecchi C., Chiti F., Colantonio S., D'Andrea C., De Angelis M., Moroni D., Nacmias B., Pascali M. A., Sorbi S., Matteini P.
The cerebrospinal fluid of 21 subjects who received a clinical diagnosis of Alzheimer’s disease (AD) as well as of 22 pathological controls has been collected and analysed by Raman spectroscopy (RS). We investigated whether the Raman spectra could be used to distinguish AD from controls, after a preprocessing procedure. We applied machine learning to a set of topological descriptors extracted from the spectra, achieving a high classification accuracy of 86%. Our experimentation indicates that RS and topological analysis may be a reliable and effective combination to confirm or disprove a clinical diagnosis of Alzheimer’s disease. The following steps will aim at leveraging the intrinsic interpretability of the topological data analysis to characterize the AD subtypes, e.g. by identifying the bands of the Raman spectrum relevant for AD detection, possibly increasing and/or confirming the knowledge about the precise molecular events and biological pathways behind the Alzheimer’s disease.Source: JOURNAL OF THE FRANKLIN INSTITUTE, vol. 361 (issue 18)
DOI: 10.1016/j.jfranklin.2024.107249
Project(s): Proteomics, RAdiomics & Machine learning-integrated strategy for precision medicine for Alzheimer’s
Metrics:
See at:
IRIS Cnr | IRIS Cnr | IRIS Cnr | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences
Mylona E., Zaridis D. I., Kalantzopoulos C., Tachos N. S., Regge D., Papanikolaou N., Tsiknakis M., Marias K., Marquez R., Henne T., Saillant C., Mora J. M., Pastor A. J., Agraniotis D., Pollalis C., Giavri Z., Hernandez W., Correia J., Bridge C., Kalpathy-Cramer J., Carloni G., Berti A., Germanese D., Del Corso G., Pachetti E., Pascali M. A., Colantonio S., Napolitano V., Maimone G., Cappello G., Mazzetti S., Giannini V., García-Martí G., Jacobs T., Doran S., Ribeiro A., Vit S., Emsley R., Koh D. M., Georgios G., Vasilis K., Slidevska K., Untanas A., Briediene R., Usinskiene J., Vilanova J. C., Karcaaltincaba M., Atak F., Karaosmanoglu A. D., Özmen M., Akata D., Nan, Mendola V., Tumminello L., Aringhieri G., Neri E., Marfil M., Navarro S., Ribas G., Cerdá-Alberich L., Martí-Bonmatí L., Futterer J., Twilt J. J., Saha A., De Rooij M., Huisman H., Chambel M., Rodrigues N., Rodrigues A. C., Verde A. C., De Almeida J. G., Dimitriadis A., Kalliatakis G., Trivizakis E., Kalokyri V., Sfakianakis S., Fotiadis D. I.
Objectives: Radiomics-based analyses encompass multiple steps, leading to ambiguity regarding the optimal approaches for enhancing model performance. This study compares the effect of several feature selection methods, machine learning (ML) classifiers, and sources of radiomic features, on models' performance for the diagnosis of clinically significant prostate cancer (csPCa) from bi-parametric MRI. Methods: Two multi-centric datasets, with 465 and 204 patients each, were used to extract 1246 radiomic features per patient and MRI sequence. Ten feature selection methods, such as Boruta, mRMRe, ReliefF, recursive feature elimination (RFE), random forest (RF) variable importance, L1-lasso, etc., four ML classifiers, namely SVM, RF, LASSO, and boosted generalized linear model (GLM), and three sets of radiomics features, derived from T2w images, ADC maps, and their combination, were used to develop predictive models of csPCa. Their performance was evaluated in a nested cross-validation and externally, using seven performance metrics. Results: In total, 480 models were developed. In nested cross-validation, the best model combined Boruta with Boosted GLM (AUC = 0.71, F1 = 0.76). In external validation, the best model combined L1-lasso with boosted GLM (AUC = 0.71, F1 = 0.47). Overall, Boruta, RFE, L1-lasso, and RF variable importance were the top-performing feature selection methods, while the choice of ML classifier didn't significantly affect the results. The ADC-derived features showed the highest discriminatory power with T2w-derived features being less informative, while their combination did not lead to improved performance. Conclusion: The choice of feature selection method and the source of radiomic features have a profound effect on the models' performance for csPCa diagnosis. Critical relevance statement: This work may guide future radiomic research, paving the way for the development of more effective and reliable radiomic models; not only for advancing prostate cancer diagnostic strategies, but also for informing broader applications of radiomics in different medical contexts. Key points: Radiomics is a growing field that can still be optimized. Feature selection method impacts radiomics models' performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. ADC-derived radiomic features yield more robust models compared to T2w-derived radiomic features.Source: INSIGHTS INTO IMAGING, vol. 15 (issue 1)
DOI: 10.1186/s13244-024-01783-9
Project(s): ProCAncer-I
Metrics:
Mylona E., Zaridis D. I., Kalantzopoulos C., Tachos N. S., Regge D., Papanikolaou N., Tsiknakis M., Marias K., Marquez R., Henne T., Saillant C., Mora J. M., Pastor A. J., Agraniotis D., Pollalis C., Giavri Z., Hernandez W., Correia J., Bridge C., Kalpathy-Cramer J., Carloni G., Berti A., Germanese D., Del Corso G., Pachetti E., Pascali M. A., Colantonio S., Napolitano V., Maimone G., Cappello G., Mazzetti S., Giannini V., García-Martí G., Jacobs T., Doran S., Ribeiro A., Vit S., Emsley R., Koh D. M., Georgios G., Vasilis K., Slidevska K., Untanas A., Briediene R., Usinskiene J., Vilanova J. C., Karcaaltincaba M., Atak F., Karaosmanoglu A. D., Özmen M., Akata D., Nan, Mendola V., Tumminello L., Aringhieri G., Neri E., Marfil M., Navarro S., Ribas G., Cerdá-Alberich L., Martí-Bonmatí L., Futterer J., Twilt J. J., Saha A., De Rooij M., Huisman H., Chambel M., Rodrigues N., Rodrigues A. C., Verde A. C., De Almeida J. G., Dimitriadis A., Kalliatakis G., Trivizakis E., Kalokyri V., Sfakianakis S., Fotiadis D. I.
Objectives: Radiomics-based analyses encompass multiple steps, leading to ambiguity regarding the optimal approaches for enhancing model performance. This study compares the effect of several feature selection methods, machine learning (ML) classifiers, and sources of radiomic features, on models' performance for the diagnosis of clinically significant prostate cancer (csPCa) from bi-parametric MRI. Methods: Two multi-centric datasets, with 465 and 204 patients each, were used to extract 1246 radiomic features per patient and MRI sequence. Ten feature selection methods, such as Boruta, mRMRe, ReliefF, recursive feature elimination (RFE), random forest (RF) variable importance, L1-lasso, etc., four ML classifiers, namely SVM, RF, LASSO, and boosted generalized linear model (GLM), and three sets of radiomics features, derived from T2w images, ADC maps, and their combination, were used to develop predictive models of csPCa. Their performance was evaluated in a nested cross-validation and externally, using seven performance metrics. Results: In total, 480 models were developed. In nested cross-validation, the best model combined Boruta with Boosted GLM (AUC = 0.71, F1 = 0.76). In external validation, the best model combined L1-lasso with boosted GLM (AUC = 0.71, F1 = 0.47). Overall, Boruta, RFE, L1-lasso, and RF variable importance were the top-performing feature selection methods, while the choice of ML classifier didn't significantly affect the results. The ADC-derived features showed the highest discriminatory power with T2w-derived features being less informative, while their combination did not lead to improved performance. Conclusion: The choice of feature selection method and the source of radiomic features have a profound effect on the models' performance for csPCa diagnosis. Critical relevance statement: This work may guide future radiomic research, paving the way for the development of more effective and reliable radiomic models; not only for advancing prostate cancer diagnostic strategies, but also for informing broader applications of radiomics in different medical contexts. Key points: Radiomics is a growing field that can still be optimized. Feature selection method impacts radiomics models' performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. ADC-derived radiomic features yield more robust models compared to T2w-derived radiomic features.Source: INSIGHTS INTO IMAGING, vol. 15 (issue 1)
DOI: 10.1186/s13244-024-01783-9
Project(s): ProCAncer-I
Metrics:
See at:
Insights into Imaging | IRIS Cnr | CNR IRIS | insightsimaging.springeropen.com | IRIS Cnr | IRIS Cnr | IRIS Cnr | CNR IRIS
2024
Conference article
Restricted
Radiomics-based reliable predictions of side effects after radiotherapy for prostate cancer
Del Corso G., Pachetti E., Buongiorno R., Rodrigues A. C., Germanese D., Pascali M. A., Almeida J., Rodrigues N., Tsiknakis M., Papanikolaou N., Regge D., Marias K., Consortium Procancer-I, Colantonio S.
This work offers insight into the effectiveness of probabilistic models, specifically those based on ensemble approximations, in predicting adverse side effects following radiotherapy for prostate cancer. We trained a random forest model on radiomic features from 134 T2-weighted Magnetic Resonance (MRI) images of the prostate gland to identify patients experiencing acute or chronic rectal and urinary toxicity (AU-ROC ranging from 61.4% for endorectal coil acquisitions to 70.8% for the full dataset). We evaluated the reliability of the predictions using an ensemble approximation of simplified random forests obtained by an adaptive procedure of random subsampling of the training data. We used this reliability score to define a not-confident class and then recompute performance metrics more in accordance with a probabilistic approach. The outcomes we obtained (up to 7.9% increase in accuracy) indicate the approximated probabilistic models pledge more reliable predictions, thus being suitable for further investigation.DOI: 10.1109/isbi56570.2024.10635233
Project(s): ProCAncer-I
Metrics:
Del Corso G., Pachetti E., Buongiorno R., Rodrigues A. C., Germanese D., Pascali M. A., Almeida J., Rodrigues N., Tsiknakis M., Papanikolaou N., Regge D., Marias K., Consortium Procancer-I, Colantonio S.
This work offers insight into the effectiveness of probabilistic models, specifically those based on ensemble approximations, in predicting adverse side effects following radiotherapy for prostate cancer. We trained a random forest model on radiomic features from 134 T2-weighted Magnetic Resonance (MRI) images of the prostate gland to identify patients experiencing acute or chronic rectal and urinary toxicity (AU-ROC ranging from 61.4% for endorectal coil acquisitions to 70.8% for the full dataset). We evaluated the reliability of the predictions using an ensemble approximation of simplified random forests obtained by an adaptive procedure of random subsampling of the training data. We used this reliability score to define a not-confident class and then recompute performance metrics more in accordance with a probabilistic approach. The outcomes we obtained (up to 7.9% increase in accuracy) indicate the approximated probabilistic models pledge more reliable predictions, thus being suitable for further investigation.DOI: 10.1109/isbi56570.2024.10635233
Project(s): ProCAncer-I
Metrics:
2024
Conference article
Open Access
Facial landmark identification and data preparation can significantly improve the extraction of newborns' facial features
Del Corso G., Germanese D., Pascali M. A., Bardelli S., Cuttano A., Festante F., Guzzetta A., Rocchitelli L., Colantonio S.
Automatic extraction of facial feature can provide valuable information on the health of newborns. However, determining an optimal facial features extraction strategy, especially for preterm infants, is a challenging task due to significant differences in facial morphology and frequent pose changes. In this work, we collected video data from 10 newborns (8 preterm, 2 at term, ≤ 4 weeks post term equivalent age), obtaining a novel dataset of over 41, 000 labeled frames (Open Mouth, Closed Mouth, Tongue Protrusion). On the collected images, we applied a strong data preparation procedure (including mouth localization, cropping, and reorientation with models trained on adults), an adaptive image normalization strategy, and a proper data augmentation scheme. Thus, we trained a convolutional classifier with a large number of trainable parameters (i.e., ~1.2 million), coupled with multiple criteria to avoid overspecialization and consequent loss of generalization capability. This approach allows for highly reliable results (accuracy, precision, and recall over 92% on unseen data) and generalizes well to newborns with significantly different characteristics, even without including time-dependent information in the analysis. Therefore, these results prove that proper data preparation can narrow the gap between the classification of neonatal and adult facial features, allowing the integration of methods originally developed for adults into the complex setting of preterm infant analysis.DOI: 10.1109/fg59268.2024.10581971
Metrics:
Del Corso G., Germanese D., Pascali M. A., Bardelli S., Cuttano A., Festante F., Guzzetta A., Rocchitelli L., Colantonio S.
Automatic extraction of facial feature can provide valuable information on the health of newborns. However, determining an optimal facial features extraction strategy, especially for preterm infants, is a challenging task due to significant differences in facial morphology and frequent pose changes. In this work, we collected video data from 10 newborns (8 preterm, 2 at term, ≤ 4 weeks post term equivalent age), obtaining a novel dataset of over 41, 000 labeled frames (Open Mouth, Closed Mouth, Tongue Protrusion). On the collected images, we applied a strong data preparation procedure (including mouth localization, cropping, and reorientation with models trained on adults), an adaptive image normalization strategy, and a proper data augmentation scheme. Thus, we trained a convolutional classifier with a large number of trainable parameters (i.e., ~1.2 million), coupled with multiple criteria to avoid overspecialization and consequent loss of generalization capability. This approach allows for highly reliable results (accuracy, precision, and recall over 92% on unseen data) and generalizes well to newborns with significantly different characteristics, even without including time-dependent information in the analysis. Therefore, these results prove that proper data preparation can narrow the gap between the classification of neonatal and adult facial features, allowing the integration of methods originally developed for adults into the complex setting of preterm infant analysis.DOI: 10.1109/fg59268.2024.10581971
Metrics:
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IRIS Cnr | IRIS Cnr | IRIS Cnr | doi.org | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
From Covid-19 detection to cancer grading: how medical-AI is boosting clinical diagnostics and may improve treatment
Berti A., Buongiorno R., Carloni G., Caudai C., Conti F., Del Corso G., Germanese D., Moroni D., Pachetti E., Pascali M. A., Colantonio S.
The integration of artificial intelligence (AI) into medical imaging has guided an era of transformation in healthcare. This paper presents the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in medical imaging. From the convolutional neural network-based segmentation of Covid-19 lung patterns to the radiomic signature for benign/malignant breast nodule discrimination, to the automatic grading of prostate cancer, this work highlights the paradigm shift that AI has brought to medical imaging, revolutionizing diagnosis and patient care.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 336-341. Naples, Italy, 29-30/05/2024
Berti A., Buongiorno R., Carloni G., Caudai C., Conti F., Del Corso G., Germanese D., Moroni D., Pachetti E., Pascali M. A., Colantonio S.
The integration of artificial intelligence (AI) into medical imaging has guided an era of transformation in healthcare. This paper presents the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in medical imaging. From the convolutional neural network-based segmentation of Covid-19 lung patterns to the radiomic signature for benign/malignant breast nodule discrimination, to the automatic grading of prostate cancer, this work highlights the paradigm shift that AI has brought to medical imaging, revolutionizing diagnosis and patient care.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 336-341. Naples, Italy, 29-30/05/2024
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Other
Open Access
EchoLocator: an open source Python package for the standardisation of echographic images in multicentre analysis
Del Corso G., De Rosa L., Pascali M. A., Faita F., Colantonio S.
In this technical report, we provide a fully automated preprocessing package, developed entirely in Python 3.6, to reduce such heterogeneity in US images. This package allows the automatic removal of echographic watermarks, cropping and centering the echographic cone. Moreover, the echographic cone is converted in a rectangular region. The EchoLocator package is freely available on GitHub.DOI: 10.32079/isti-tr-2024/003
Metrics:
Del Corso G., De Rosa L., Pascali M. A., Faita F., Colantonio S.
In this technical report, we provide a fully automated preprocessing package, developed entirely in Python 3.6, to reduce such heterogeneity in US images. This package allows the automatic removal of echographic watermarks, cropping and centering the echographic cone. Moreover, the echographic cone is converted in a rectangular region. The EchoLocator package is freely available on GitHub.DOI: 10.32079/isti-tr-2024/003
Metrics:
2024
Journal article
Open Access
ANN uncertainty estimates in assessing fatty liver content from ultrasound data
G. Del Corso, M. A. Pascali, C. Caudai, L. De Rosa, A. Salvati, M. Mancini, L. Ghiadoni, F. Bonino, M. R. Brunetto, S. Colantonio, F. Faita
Background and objective This article uses three different probabilistic convolutional architectures applied to ultrasound image analysis for grading Fatty Liver Content (FLC) in Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) patients. Steatosis is a new silent epidemic and its accurate measurement is an impelling clinical need, not only for hepatologists, but also for experts in metabolic and cardiovascular diseases. This paper aims to provide a robust comparison between different uncertainty quantification strategies to identify advantages and drawbacks in a real clinical setting. Methods We used a classical Convolutional Neural Network, a Monte Carlo Dropout, and a Bayesian Convolutional Neural Network with the goal of not only comparing the goodness of the predictions, but also to have access to an evaluation of the uncertainty associated with the outputs. Results We found that even if the prediction based on a single ultrasound view is reliable (relative RMSE [5.93%-12.04%]), networks based on two ultrasound views outperform them (relative RMSE [5.35%-5.87%]). In addition, the results show that the introduction of a “not confident” category contributes to increase the percentage of correctly predicted cases and to decrease the percentage of mispredicted cases, especially for semi-intrusive methods. Conclusions The possibility of having access to information about the confidence with which the network produces its outputs is a great advantage, both from the point of view of physicians who want to use neural networks as computer-aided diagnosis, and for developers who want to limit overfitting and obtain information about dataset problems in terms of out-of-distribution detection.Source: COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, vol. 24, pp. 603-610
DOI: 10.1016/j.csbj.2024.09.021
Metrics:
G. Del Corso, M. A. Pascali, C. Caudai, L. De Rosa, A. Salvati, M. Mancini, L. Ghiadoni, F. Bonino, M. R. Brunetto, S. Colantonio, F. Faita
Background and objective This article uses three different probabilistic convolutional architectures applied to ultrasound image analysis for grading Fatty Liver Content (FLC) in Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) patients. Steatosis is a new silent epidemic and its accurate measurement is an impelling clinical need, not only for hepatologists, but also for experts in metabolic and cardiovascular diseases. This paper aims to provide a robust comparison between different uncertainty quantification strategies to identify advantages and drawbacks in a real clinical setting. Methods We used a classical Convolutional Neural Network, a Monte Carlo Dropout, and a Bayesian Convolutional Neural Network with the goal of not only comparing the goodness of the predictions, but also to have access to an evaluation of the uncertainty associated with the outputs. Results We found that even if the prediction based on a single ultrasound view is reliable (relative RMSE [5.93%-12.04%]), networks based on two ultrasound views outperform them (relative RMSE [5.35%-5.87%]). In addition, the results show that the introduction of a “not confident” category contributes to increase the percentage of correctly predicted cases and to decrease the percentage of mispredicted cases, especially for semi-intrusive methods. Conclusions The possibility of having access to information about the confidence with which the network produces its outputs is a great advantage, both from the point of view of physicians who want to use neural networks as computer-aided diagnosis, and for developers who want to limit overfitting and obtain information about dataset problems in terms of out-of-distribution detection.Source: COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, vol. 24, pp. 603-610
DOI: 10.1016/j.csbj.2024.09.021
Metrics:
See at:
Computational and Structural Biotechnology Journal | IRIS - Institutional Research Information System of the University of Trento | IRIS Cnr | IRIS Cnr | IRIS - Institutional Research Information System of the University of Trento | IRIS - Institutional Research Information System of the University of Trento | CNR IRIS
2024
Journal article
Open Access
Adaptive machine learning approach for importance evaluation of multimodal breast cancer radiomic features
Del Corso G., Germanese D., Caudai C., Anastasi G., Belli P., Formica A., Nicolucci A., Palma S., Pascali M. A., Pieroni S., Trombadori C., Colantonio S., Franchini M., Molinaro S.
Breast cancer holds the highest diagnosis rate among female tumors and is the leading cause of death among women. Quantitative analysis of radiological images shows the potential to address several medical challenges, including the early detection and classification of breast tumors. In the P.I.N.K study, 66 women were enrolled. Their paired Automated Breast Volume Scanner (ABVS) and Digital Breast Tomosynthesis (DBT) images, annotated with cancerous lesions, populated the first ABVS+DBT dataset. This enabled not only a radiomic analysis for the malignant vs. benign breast cancer classification, but also the comparison of the two modalities. For this purpose, the models were trained using a leave-one-out nested cross-validation strategy combined with a proper threshold selection approach. This approach provides statistically significant results even with medium-sized data sets. Additionally it provides distributional variables of importance, thus identifying the most informative radiomic features. The analysis proved the predictive capacity of radiomic models even using a reduced number of features. Indeed, from tomography we achieved AUC-ROC 89.9% using 19 features and 92.1% using 7 of them; while from ABVS we attained an AUC-ROC of 72.3% using 22 features and 85.8% using only 3 features. Although the predictive power of DBT outperforms ABVS, when comparing the predictions at the patient level, only 8.7% of lesions are misclassified by both methods, suggesting a partial complementarity. Notably, promising results (AUC-ROC ABVS-DBT 71.8% - 74.1% ) were achieved using non-geometric features, thus opening the way to the integration of virtual biopsy in medical routine.Source: JOURNAL OF IMAGING INFORMATICS IN MEDICINE, vol. 37 (issue 4), pp. 1642-1651
DOI: 10.1007/s10278-024-01064-3
Project(s): "Mortalità Zero - verso la personalizzazione degli interventi diagnostici"
Metrics:
Del Corso G., Germanese D., Caudai C., Anastasi G., Belli P., Formica A., Nicolucci A., Palma S., Pascali M. A., Pieroni S., Trombadori C., Colantonio S., Franchini M., Molinaro S.
Breast cancer holds the highest diagnosis rate among female tumors and is the leading cause of death among women. Quantitative analysis of radiological images shows the potential to address several medical challenges, including the early detection and classification of breast tumors. In the P.I.N.K study, 66 women were enrolled. Their paired Automated Breast Volume Scanner (ABVS) and Digital Breast Tomosynthesis (DBT) images, annotated with cancerous lesions, populated the first ABVS+DBT dataset. This enabled not only a radiomic analysis for the malignant vs. benign breast cancer classification, but also the comparison of the two modalities. For this purpose, the models were trained using a leave-one-out nested cross-validation strategy combined with a proper threshold selection approach. This approach provides statistically significant results even with medium-sized data sets. Additionally it provides distributional variables of importance, thus identifying the most informative radiomic features. The analysis proved the predictive capacity of radiomic models even using a reduced number of features. Indeed, from tomography we achieved AUC-ROC 89.9% using 19 features and 92.1% using 7 of them; while from ABVS we attained an AUC-ROC of 72.3% using 22 features and 85.8% using only 3 features. Although the predictive power of DBT outperforms ABVS, when comparing the predictions at the patient level, only 8.7% of lesions are misclassified by both methods, suggesting a partial complementarity. Notably, promising results (AUC-ROC ABVS-DBT 71.8% - 74.1% ) were achieved using non-geometric features, thus opening the way to the integration of virtual biopsy in medical routine.Source: JOURNAL OF IMAGING INFORMATICS IN MEDICINE, vol. 37 (issue 4), pp. 1642-1651
DOI: 10.1007/s10278-024-01064-3
Project(s): "Mortalità Zero - verso la personalizzazione degli interventi diagnostici"
Metrics:
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Journal of Imaging Informatics in Medicine | IRIS Cnr | IRIS Cnr | CNR IRIS | CNR IRIS