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2025
Other
Open Access
U-ProBE: Uncertainty Probabilistic Bayesian Estimate
Bandini L., Del Corso G., Colantonio S., Caudai C.
In this technical report we have designed and developed a Python software suite (U-ProBE: Uncertainty Probabilistic Bayesian Estimate) for analyzing Deep Learning models with predictions affected by uncertainty (i.e., Bayesian Probabilistic Models). The suite is equipped with an intuitive graphical interface that is simple to use even for non-experts and designed to support a growing pool of users who need to evaluate a model’s performance and, above all, its uncertainty.DOI: 10.32079/isti-tr-2025/006
Project(s): ProCAncer-I
Metrics:
Bandini L., Del Corso G., Colantonio S., Caudai C.
In this technical report we have designed and developed a Python software suite (U-ProBE: Uncertainty Probabilistic Bayesian Estimate) for analyzing Deep Learning models with predictions affected by uncertainty (i.e., Bayesian Probabilistic Models). The suite is equipped with an intuitive graphical interface that is simple to use even for non-experts and designed to support a growing pool of users who need to evaluate a model’s performance and, above all, its uncertainty.DOI: 10.32079/isti-tr-2025/006
Project(s): ProCAncer-I
Metrics:
2025
Other
Open Access
Progettazione di codici 2D per la tracciabilità circolare di cavi
Cuttano M. -L., Marrazzini L., Del Corso G., Moroni D.
Questa relazione descrive l’attività di tirocinio svolta presso il “Laboratorio Segnali e Immagini” dell’Istituto di Scienza e Tecnologia dell’Informazione “A. Faedo” CNR-ISTI su “CircularEconomyCable”, progetto condotto dall’azienda produttrice di cavi “Tratos S.p.A.”. Il progetto, della durata prevista di due anni, mira allo sviluppo e prototipazione di innovativi cavi elettrici e in fibra ottica ad elevata sostenibilità, tracciabilità e visibilità, per consentire la piena attuazione dei principi dell’economia circolare e della transizione digitale nei settori del trasporto energia e della connettività dati.
Cuttano M. -L., Marrazzini L., Del Corso G., Moroni D.
Questa relazione descrive l’attività di tirocinio svolta presso il “Laboratorio Segnali e Immagini” dell’Istituto di Scienza e Tecnologia dell’Informazione “A. Faedo” CNR-ISTI su “CircularEconomyCable”, progetto condotto dall’azienda produttrice di cavi “Tratos S.p.A.”. Il progetto, della durata prevista di due anni, mira allo sviluppo e prototipazione di innovativi cavi elettrici e in fibra ottica ad elevata sostenibilità, tracciabilità e visibilità, per consentire la piena attuazione dei principi dell’economia circolare e della transizione digitale nei settori del trasporto energia e della connettività dati.
2025
Other
Open Access
Redundant 2D Code: generation of custom bidimensional codes on the Reed-Solomon error correction algorithm
Cuttano M. L., Del Corso G., Moroni D.
This technical report details Redundant 2D Code, an open-source software written in Python that generates rectangular binary matrices. It allows for great flexibility in terms of the dimensions, number, and shape of positional markers, which are fundamental to the reading phase. Similarly to existing QR-codes, the customized code is built using state-of-the-art the Reed- Solomon error correction algorithm to make it more resistant to damages and information loss. However, this Python package enables full customization of redundancy and shape characteristics, providing flexible tools applicable across multiple domains, particularly in industrial settings, where a one-size-fits-all approach is often unsuitable due to product variability.DOI: 10.32079/isti-tr-2025/016
Metrics:
Cuttano M. L., Del Corso G., Moroni D.
This technical report details Redundant 2D Code, an open-source software written in Python that generates rectangular binary matrices. It allows for great flexibility in terms of the dimensions, number, and shape of positional markers, which are fundamental to the reading phase. Similarly to existing QR-codes, the customized code is built using state-of-the-art the Reed- Solomon error correction algorithm to make it more resistant to damages and information loss. However, this Python package enables full customization of redundancy and shape characteristics, providing flexible tools applicable across multiple domains, particularly in industrial settings, where a one-size-fits-all approach is often unsuitable due to product variability.DOI: 10.32079/isti-tr-2025/016
Metrics:
2025
Other
Open Access
Time series scribe: open-source platform for enhanced annotation in multi-channel signal processing
Del Corso G., Kanka S., Bardelli S., Visaggi P., Colantonio S.
Multi-channel temporal signal annotation for training predictive models requires open source software tools that are easy to use and adaptable to different real-world applications. This is especially true in the analysis of biological and medical signals, where the annotation process is not carried out by data scientists. Therefore, this technical report proposes an open source Python software package that allows the annotation of time signals through the use of an intuitive graphical interface. In particular, the annotation of the refluxes in a ph-impedance signal has been taken as a case study.DOI: 10.32079/isti-tr-2025/001
Metrics:
Del Corso G., Kanka S., Bardelli S., Visaggi P., Colantonio S.
Multi-channel temporal signal annotation for training predictive models requires open source software tools that are easy to use and adaptable to different real-world applications. This is especially true in the analysis of biological and medical signals, where the annotation process is not carried out by data scientists. Therefore, this technical report proposes an open source Python software package that allows the annotation of time signals through the use of an intuitive graphical interface. In particular, the annotation of the refluxes in a ph-impedance signal has been taken as a case study.DOI: 10.32079/isti-tr-2025/001
Metrics:
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
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
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
Shedding light on uncertainties in machine learning: formal derivation and optimal model selection
Del Corso G., Colantonio S., Caudai C.
The concept of uncertainty has always been important in the field of mathematical modeling. In particular, the growing application of Machine Learning and Deep Learning methods in many scientific fields has led to the implementation and use of new uncertainty quantification techniques aimed at distinguishing between reliable and unreliable predictions. However, the novelty of this discipline and the plethora of articles produced, ranging from theoretical results to purely applied experiments, has resulted in a very fragmented and cluttered literature. In this review, we have attempted to combine the well-established mathematical background of the Bayesian framework with the practical aspect of modern state-of-the-art emerging techniques in order to meet the urgent need for clarity on key concepts related to uncertainty quantification. First, we introduced the different sources of uncertainty, ranging from epistemic/reducible to aleatoric/irreducible, providing both a rigorous mathematical derivation and several examples to facilitate understanding. The review then details some of the most important techniques for uncertainty quantification. These methods are compared in terms of their advantages and drawbacks and classified in terms of their intrusiveness, in order to provide the practitioner with a useful vademecum for selecting the optimal model depending on the application context.Source: JOURNAL OF THE FRANKLIN INSTITUTE, vol. 362 (issue 3)
DOI: 10.1016/j.jfranklin.2025.107548
Project(s): ProCAncer-I, FAITH
Metrics:
Del Corso G., Colantonio S., Caudai C.
The concept of uncertainty has always been important in the field of mathematical modeling. In particular, the growing application of Machine Learning and Deep Learning methods in many scientific fields has led to the implementation and use of new uncertainty quantification techniques aimed at distinguishing between reliable and unreliable predictions. However, the novelty of this discipline and the plethora of articles produced, ranging from theoretical results to purely applied experiments, has resulted in a very fragmented and cluttered literature. In this review, we have attempted to combine the well-established mathematical background of the Bayesian framework with the practical aspect of modern state-of-the-art emerging techniques in order to meet the urgent need for clarity on key concepts related to uncertainty quantification. First, we introduced the different sources of uncertainty, ranging from epistemic/reducible to aleatoric/irreducible, providing both a rigorous mathematical derivation and several examples to facilitate understanding. The review then details some of the most important techniques for uncertainty quantification. These methods are compared in terms of their advantages and drawbacks and classified in terms of their intrusiveness, in order to provide the practitioner with a useful vademecum for selecting the optimal model depending on the application context.Source: JOURNAL OF THE FRANKLIN INSTITUTE, vol. 362 (issue 3)
DOI: 10.1016/j.jfranklin.2025.107548
Project(s): ProCAncer-I
, FAITH Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS
2025
Journal article
Open Access
Systematic review: use of artificial intelligence and unmet needs in eosinophilic oesophagitis
Castagnaro E., Felici E., Spaccapelo R., Amoroso M., Moretti D., Saab J. P., Stassaldi A., Tassone A., Borrelli O., De Bortoli N., Del Corso G., Gaynor E., Goh L., Oliva S., Rea F., Renzo S., Romano C., Savarino E. V., Visaggi P.
Background: Artificial intelligence (AI) has been applied extensively to eosinophilic oesophagitis (EoE), but its clinical impact and comprehensiveness remain unclear. Aims: To summarise the state of the art of applications of AI in EoE, identify gaps in the literature and disclose unmet needs. Methods: We performed a systematic review of applications of AI in diagnosis, prognosis, precision medicine and follow-up in EoE. We searched MEDLINE, Embase and Embase Classic (via Ovid) from inception until 28th February 2025. Clinical trials, cohort, case-control, cross-sectional and case series studies were eligible for inclusion. Results: We identified 28 studies on AI in EoE, with 18 focusing on diagnosis. AI was used for histology image analysis in digital pathology in seven and endoscopic image analysis in three studies. AI-based non-invasive diagnostic predictive models showed high accuracy in detecting EoE. As a prognostic tool, AI showed potential in predicting food impaction, histologic disease severity and fibrosis. AI was used in precision medicine for biomarker discovery, endotype prediction based on transcriptomics and treatment personalisation, as well as in disease monitoring and follow-up. Unmet needs included insufficient studies on children/adolescents, prediction of treatment response and improvement of study designs. Conclusions: Although AI showed promise at improving EoE management across various domains, most tools require validation on larger, independent and prospective cohorts. Several challenges, including algorithmic bias, data privacy, explainability and regulatory hurdles, need to be addressed for clinical implementation. Future studies should focus on developing non-invasive diagnostic tools for younger populations, treatment response prediction and disease monitoring.Source: ALIMENTARY PHARMACOLOGY & THERAPEUTICS
DOI: 10.1111/apt.70222
Metrics:
Castagnaro E., Felici E., Spaccapelo R., Amoroso M., Moretti D., Saab J. P., Stassaldi A., Tassone A., Borrelli O., De Bortoli N., Del Corso G., Gaynor E., Goh L., Oliva S., Rea F., Renzo S., Romano C., Savarino E. V., Visaggi P.
Background: Artificial intelligence (AI) has been applied extensively to eosinophilic oesophagitis (EoE), but its clinical impact and comprehensiveness remain unclear. Aims: To summarise the state of the art of applications of AI in EoE, identify gaps in the literature and disclose unmet needs. Methods: We performed a systematic review of applications of AI in diagnosis, prognosis, precision medicine and follow-up in EoE. We searched MEDLINE, Embase and Embase Classic (via Ovid) from inception until 28th February 2025. Clinical trials, cohort, case-control, cross-sectional and case series studies were eligible for inclusion. Results: We identified 28 studies on AI in EoE, with 18 focusing on diagnosis. AI was used for histology image analysis in digital pathology in seven and endoscopic image analysis in three studies. AI-based non-invasive diagnostic predictive models showed high accuracy in detecting EoE. As a prognostic tool, AI showed potential in predicting food impaction, histologic disease severity and fibrosis. AI was used in precision medicine for biomarker discovery, endotype prediction based on transcriptomics and treatment personalisation, as well as in disease monitoring and follow-up. Unmet needs included insufficient studies on children/adolescents, prediction of treatment response and improvement of study designs. Conclusions: Although AI showed promise at improving EoE management across various domains, most tools require validation on larger, independent and prospective cohorts. Several challenges, including algorithmic bias, data privacy, explainability and regulatory hurdles, need to be addressed for clinical implementation. Future studies should focus on developing non-invasive diagnostic tools for younger populations, treatment response prediction and disease monitoring.Source: ALIMENTARY PHARMACOLOGY & THERAPEUTICS
DOI: 10.1111/apt.70222
Metrics:
See at:
CNR IRIS | onlinelibrary.wiley.com | CNR IRIS | CNR IRIS
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
Other
Open Access
Tecniche di creazione dataset e modelli IA in regime di scarsità dei dati
Bulotta D., Del Corso G., Leone G. R.
Questo documento riporta il resoconto delle attività dello studente Davide Bulotta du- rante il tirocinio extracurriculare svolto presso lo Istituto di Scienza e Tecnologia dell’Informazione nel periodo Aprile 2024 - Marzo 2025. Il tirocinio è connesso con il progetto europeo F.A.I.T.H. (Fostering Artificial Intelligence Trust for Humans)[1], il cui obiettivo è quello di definire ed implementare degli strumenti ad hoc (FAITH Risk Management Framework) per misurare e migliorare l’affidabilità dei sistemi che utilizzano modelli di Intelligenza Artificiale (IA). Tale Framework è implementato, dimostrato e perfezionato tramite una selezione rappresentativa di sette progetti pilota su larga scala in ambiti critici, tra cui la mobilità nel trasporto pubblico. Lo ISTC-CNR si occupa di quest’ultimo dominio ed in particolare dell’utilizzo di sistemi di visione artificiale a bordo di treni regionali del vet- tore nazionale Trenitalia. Si opera in un ambiente in cui non è possibile ingegnerizzare al meglio la scena e vi è il divieto di effettuare registrazioni per rispetto del GDPR (ovvero le registrazioni effettuate sono visionabili solo da autorità di pubblica sicurezza). Lo studio effettuato affronta le sfide teoriche e implementative associate all’implementazione di sistemi di visione artificiale in domini caratterizzati da elevata scarsità di dati e rigidi vincoli hardware. Il fulcro della ricerca propone una metodologia semi-supervisionata definita “Zero-shot Annotation Coaching”, che formalizza una pipeline di distillazione della conoscenza. In questo contesto, un rilevatore di vocabolario aperto basato su Vision Transformer, in particolare OWLv2, funge da modello “insegnante”, sfruttando le sue capacità di generalizzazione semantica per generare pseudo-etichette da flussi video non annotati. Questo processo trasferisce efficacemente la comprensione semantica dell’architettura del trasformatore in un set di dati strutturato, consentendo l’addestramento supervisionato di un modello “studente”, YOLOv8, ottimizzato architettonicamente per l’inferenza a bassa latenza piuttosto che per l’ampiezza semantica open-world.
Bulotta D., Del Corso G., Leone G. R.
Questo documento riporta il resoconto delle attività dello studente Davide Bulotta du- rante il tirocinio extracurriculare svolto presso lo Istituto di Scienza e Tecnologia dell’Informazione nel periodo Aprile 2024 - Marzo 2025. Il tirocinio è connesso con il progetto europeo F.A.I.T.H. (Fostering Artificial Intelligence Trust for Humans)[1], il cui obiettivo è quello di definire ed implementare degli strumenti ad hoc (FAITH Risk Management Framework) per misurare e migliorare l’affidabilità dei sistemi che utilizzano modelli di Intelligenza Artificiale (IA). Tale Framework è implementato, dimostrato e perfezionato tramite una selezione rappresentativa di sette progetti pilota su larga scala in ambiti critici, tra cui la mobilità nel trasporto pubblico. Lo ISTC-CNR si occupa di quest’ultimo dominio ed in particolare dell’utilizzo di sistemi di visione artificiale a bordo di treni regionali del vet- tore nazionale Trenitalia. Si opera in un ambiente in cui non è possibile ingegnerizzare al meglio la scena e vi è il divieto di effettuare registrazioni per rispetto del GDPR (ovvero le registrazioni effettuate sono visionabili solo da autorità di pubblica sicurezza). Lo studio effettuato affronta le sfide teoriche e implementative associate all’implementazione di sistemi di visione artificiale in domini caratterizzati da elevata scarsità di dati e rigidi vincoli hardware. Il fulcro della ricerca propone una metodologia semi-supervisionata definita “Zero-shot Annotation Coaching”, che formalizza una pipeline di distillazione della conoscenza. In questo contesto, un rilevatore di vocabolario aperto basato su Vision Transformer, in particolare OWLv2, funge da modello “insegnante”, sfruttando le sue capacità di generalizzazione semantica per generare pseudo-etichette da flussi video non annotati. Questo processo trasferisce efficacemente la comprensione semantica dell’architettura del trasformatore in un set di dati strutturato, consentendo l’addestramento supervisionato di un modello “studente”, YOLOv8, ottimizzato architettonicamente per l’inferenza a bassa latenza piuttosto che per l’ampiezza semantica open-world.
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