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
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
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
2024
Other
Open Access
Stretch your tentacles, POLPO-net: a POLymorphic PrObabilistic approach to greedily approximate model uncertainties
Del Corso G., Caudai C., Kuruoglu E. E., Colantonio S.
A POLymorphic PrObabilistic approach to greedily approximate uncertainties avoiding re-training of costly deep neural networks.DOI: 10.5281/zenodo.12780350
Metrics:
Del Corso G., Caudai C., Kuruoglu E. E., Colantonio S.
A POLymorphic PrObabilistic approach to greedily approximate uncertainties avoiding re-training of costly deep neural networks.DOI: 10.5281/zenodo.12780350
Metrics:
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
Other
Open Access
U-ProBE: a graphical Python interface to handle uncertainties in deep learning models
Bandini L., Bacciu D., Del Corso G., Caudai C.
La piattaforma U-Probe mira ad essere un supporto ad un crescente numero di utilizzatori che hanno necessità di valutare le performances di un modello e soprat- tutto la sua incertezza. Per questo motivo la piattaforma è dotata di una intuitiva interfaccia grafica semplice da utilizzare anche per i non addetti ai lavori. L’analisi dell’incertezza delle predizioni di un modello di Machine Learning o Deep Learning può essere effettuata utilizzando varie tecniche. Alcune di queste sono intrusive (anche dette by design), tali tecniche vanno a modificare l’architettura in- troducendo strumenti probabilistici che possono fornire importanti indicazioni sulle caratteristiche delle predizioni, a livello di affidabilità e incertezza. Tali tecniche comprendono ad esempio le Bayesian Neural Networks, i Variational Autoencoders ed i Deep Gaussian Processes. Sono tecniche molto performanti, sia nel mitigare l’overfitting che nell’uncertainty quantification, di contro sono però molto costose e richiedono molte risorse di calcolo e di tempo per l’allenamento dei modelli. Esistono poi le tecniche semi-intrusive, i cui più conosciuti rappresentanti sono i Deep En- semble; esse rappresentano una ampia classe di approcci che in generale combinano più modelli secondo criteri specifici in modo da valutare l’efficienza, l’incertezza e l’affidabilità delle predizioni senza interferire troppo con le architetture di partenza, ma richiedendo comunque un ampio dispendio di risorse. In questo lavoro abbiamo deciso di utilizzare per i nostri scopi esclusivamente metodi post-hoc, cioè non intrusivi, come il Trust Score ed il Monte Carlo Dropout, che sono in grado di fare efficaci valutazioni sull’incertezza delle predizioni quando il modello è stato già allenato, senza andare a interferire con le fasi di apprendimento o a modificare i parametri già imparati dal modello durante la back propagation. Tali metodi sono leggermente meno performanti dei metodi intrusivi, ma hanno il vantaggio di essere estremamente più rapidi e meno costosi.
Bandini L., Bacciu D., Del Corso G., Caudai C.
La piattaforma U-Probe mira ad essere un supporto ad un crescente numero di utilizzatori che hanno necessità di valutare le performances di un modello e soprat- tutto la sua incertezza. Per questo motivo la piattaforma è dotata di una intuitiva interfaccia grafica semplice da utilizzare anche per i non addetti ai lavori. L’analisi dell’incertezza delle predizioni di un modello di Machine Learning o Deep Learning può essere effettuata utilizzando varie tecniche. Alcune di queste sono intrusive (anche dette by design), tali tecniche vanno a modificare l’architettura in- troducendo strumenti probabilistici che possono fornire importanti indicazioni sulle caratteristiche delle predizioni, a livello di affidabilità e incertezza. Tali tecniche comprendono ad esempio le Bayesian Neural Networks, i Variational Autoencoders ed i Deep Gaussian Processes. Sono tecniche molto performanti, sia nel mitigare l’overfitting che nell’uncertainty quantification, di contro sono però molto costose e richiedono molte risorse di calcolo e di tempo per l’allenamento dei modelli. Esistono poi le tecniche semi-intrusive, i cui più conosciuti rappresentanti sono i Deep En- semble; esse rappresentano una ampia classe di approcci che in generale combinano più modelli secondo criteri specifici in modo da valutare l’efficienza, l’incertezza e l’affidabilità delle predizioni senza interferire troppo con le architetture di partenza, ma richiedendo comunque un ampio dispendio di risorse. In questo lavoro abbiamo deciso di utilizzare per i nostri scopi esclusivamente metodi post-hoc, cioè non intrusivi, come il Trust Score ed il Monte Carlo Dropout, che sono in grado di fare efficaci valutazioni sull’incertezza delle predizioni quando il modello è stato già allenato, senza andare a interferire con le fasi di apprendimento o a modificare i parametri già imparati dal modello durante la back propagation. Tali metodi sono leggermente meno performanti dei metodi intrusivi, ma hanno il vantaggio di essere estremamente più rapidi e meno costosi.
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
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:
See at:
Journal of Imaging Informatics in Medicine | IRIS Cnr | IRIS Cnr | CNR IRIS | CNR IRIS
2024
Other
Open Access
NA DAtabase: generator of probabilistic synthetic geometrical shape dataset
Volpini F., Caudai C., Del Corso G., Colantonio S.
In this technical report, we detail NA DA (Not-A-DAtabase), an open-source software writ- ten in Python that generates datasets of regular two-dimensional geometric shapes based on probabilistic distributions (https://github.com/GDelCorso/NA DAtabase.git). NA DA comes with an intuitive GUI (Graphical User Interface) that allows users to define shapes, colors, and distributions of features of datasets consisting of image sets and CSV files containing metadata for each element. These databases can be saved to provide a unique identifier of the dataset, allowing perfect reproducibility or easy modification of the dataset using the GUI or directly by calling the generator class. Therefore, NA DA is a tool to help and support the investigation of trustworthiness, overconfidence, uncertainty, and computation time of machine learning and deep learning models.DOI: 10.32079/isti-tr-2024/004
Metrics:
Volpini F., Caudai C., Del Corso G., Colantonio S.
In this technical report, we detail NA DA (Not-A-DAtabase), an open-source software writ- ten in Python that generates datasets of regular two-dimensional geometric shapes based on probabilistic distributions (https://github.com/GDelCorso/NA DAtabase.git). NA DA comes with an intuitive GUI (Graphical User Interface) that allows users to define shapes, colors, and distributions of features of datasets consisting of image sets and CSV files containing metadata for each element. These databases can be saved to provide a unique identifier of the dataset, allowing perfect reproducibility or easy modification of the dataset using the GUI or directly by calling the generator class. Therefore, NA DA is a tool to help and support the investigation of trustworthiness, overconfidence, uncertainty, and computation time of machine learning and deep learning models.DOI: 10.32079/isti-tr-2024/004
Metrics:
2023
Conference article
Open Access
Medical waste sorting: a computer vision approach for assisted primary sorting
Bruno A, Caudai C, Leone Gr, Martinelli M, Moroni D, Crotti F
Medical waste, i.e. waste produced during medical activities in hospitals, clinics and laboratories, represents hazardous waste whose management requires special care and high costs. However, this kind of waste contains a large fraction of highly valued materials that can enter a circular economy process. To this end, in this paper, we propose a computer vision approach for assisting in the primary sorting of med- ical waste. The feasibility of our approach is demonstrated on representative datasets we collected and made available to the community.DOI: 10.1109/icasspw59220.2023.10193520
Metrics:
Bruno A, Caudai C, Leone Gr, Martinelli M, Moroni D, Crotti F
Medical waste, i.e. waste produced during medical activities in hospitals, clinics and laboratories, represents hazardous waste whose management requires special care and high costs. However, this kind of waste contains a large fraction of highly valued materials that can enter a circular economy process. To this end, in this paper, we propose a computer vision approach for assisting in the primary sorting of med- ical waste. The feasibility of our approach is demonstrated on representative datasets we collected and made available to the community.DOI: 10.1109/icasspw59220.2023.10193520
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
Raman spectroscopy and topological machine learning for cancer grading
Conti F, D'Acunto M, Caudai C, Colantonio C, Gaeta R, Moroni D, Pascali Ma
In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system.Source: SCIENTIFIC REPORTS, vol. 13
DOI: 10.1038/s41598-023-34457-5
Metrics:
Conti F, D'Acunto M, Caudai C, Colantonio C, Gaeta R, Moroni D, Pascali Ma
In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system.Source: SCIENTIFIC REPORTS, vol. 13
DOI: 10.1038/s41598-023-34457-5
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CNR IRIS | ISTI Repository | www.nature.com | CNR IRIS
2023
Contribution to book
Open Access
Introduction to machine learning in medicine
Buongiorno R, Caudai C, Colantonio S, Germanese D
This chapter aimed to describe, as simply as possible, what Machine Learning is and how it can be used fruitfully in the medical field.DOI: 10.1007/978-3-031-25928-9_3
Metrics:
Buongiorno R, Caudai C, Colantonio S, Germanese D
This chapter aimed to describe, as simply as possible, what Machine Learning is and how it can be used fruitfully in the medical field.DOI: 10.1007/978-3-031-25928-9_3
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CNR IRIS | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
Mediterranean extensive green roof self-sustainability mediated by substrate composition and plant strategy
Vannucchi F., Bibbiani C., Caudai C., Bretzel F.
In the cultivation of extensive green roofs (EGRs), substrate composition is a key aspect together with the evaluation of suitable recycled materials. Recycling materials as amendments can improve the establishment of a self-sustainable EGR, thus providing ecosystem services and benefits from a circular economy and climate change perspective. This study investigates the effects of compost and paper sludge on water retention, substrate temperature attenuation and plant diversity in an EGR experiment. The substrates were composed of tephra (V), compost (C) and paper sludge (P) as follows: VC, as control, VPC and VP. Herbaceous species with different ecological functionality (succulents, annuals, perennials, legumes, geophytes) were sown and/or transplanted with no cultivation inputs. Plant community composition -abundance- and diversity-richness-, substrate water retention and temperature were analyzed. The VPC and VC had the same average substrate temperature, with values lower than VP. The water retention capacity was higher in VC, thanks to the presence of compost. The substrate with paper sludge (VPC and VP) showed the highest species diversity. The VPC substrate was the best compromise for EGR temperature mitigation and plant diversity improvement. Plant functional types in EGRs can be increased, and thus the biodiversity, by modulating the quality and percentage of amendments. The substrate composition can also affect water retention and substrate temperature. In addition, the use of recycling paper sludge in growing media is a winning strategy to reduce waste.Source: HORTICULTURAE, vol. 9 (issue 10)
DOI: 10.3390/horticulturae9101117
Metrics:
Vannucchi F., Bibbiani C., Caudai C., Bretzel F.
In the cultivation of extensive green roofs (EGRs), substrate composition is a key aspect together with the evaluation of suitable recycled materials. Recycling materials as amendments can improve the establishment of a self-sustainable EGR, thus providing ecosystem services and benefits from a circular economy and climate change perspective. This study investigates the effects of compost and paper sludge on water retention, substrate temperature attenuation and plant diversity in an EGR experiment. The substrates were composed of tephra (V), compost (C) and paper sludge (P) as follows: VC, as control, VPC and VP. Herbaceous species with different ecological functionality (succulents, annuals, perennials, legumes, geophytes) were sown and/or transplanted with no cultivation inputs. Plant community composition -abundance- and diversity-richness-, substrate water retention and temperature were analyzed. The VPC and VC had the same average substrate temperature, with values lower than VP. The water retention capacity was higher in VC, thanks to the presence of compost. The substrate with paper sludge (VPC and VP) showed the highest species diversity. The VPC substrate was the best compromise for EGR temperature mitigation and plant diversity improvement. Plant functional types in EGRs can be increased, and thus the biodiversity, by modulating the quality and percentage of amendments. The substrate composition can also affect water retention and substrate temperature. In addition, the use of recycling paper sludge in growing media is a winning strategy to reduce waste.Source: HORTICULTURAE, vol. 9 (issue 10)
DOI: 10.3390/horticulturae9101117
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CNR IRIS | ISTI Repository | www.mdpi.com | CNR IRIS
2023
Conference article
Open Access
Exploring the potentials and challenges of AI in supporting clinical diagnostics and remote assistance for the health and well-being of individuals
Berti A, Buongiorno R, Carloni G, Caudai C, Del Corso G, Germanese D, Pachetti E, Pascali Ma, Colantonio S
Innovative technologies powered by Artificial Intelligence have the big potential to support new models of care delivery, disease prevention and quality of life promotion. The ultimate goal is a paradigm shift towards more personalized, accessible, effective, and sustainable care and health systems. Nevertheless, despite the advances in the field over the last years, the adoption and deployment of AI technologies remains limited in clinical practice and real-world settings. This paper summarizes the 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 for exploring both the potential of AI in health and well-being as well as the challenges to their uptake in real-world settingsSource: CEUR WORKSHOP PROCEEDINGS. Pisa, Italy, 29-30/05/2023
Project(s): ProCAncer-I
Berti A, Buongiorno R, Carloni G, Caudai C, Del Corso G, Germanese D, Pachetti E, Pascali Ma, Colantonio S
Innovative technologies powered by Artificial Intelligence have the big potential to support new models of care delivery, disease prevention and quality of life promotion. The ultimate goal is a paradigm shift towards more personalized, accessible, effective, and sustainable care and health systems. Nevertheless, despite the advances in the field over the last years, the adoption and deployment of AI technologies remains limited in clinical practice and real-world settings. This paper summarizes the 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 for exploring both the potential of AI in health and well-being as well as the challenges to their uptake in real-world settingsSource: CEUR WORKSHOP PROCEEDINGS. Pisa, Italy, 29-30/05/2023
Project(s): ProCAncer-I
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2023
Conference article
Open Access
Exploring the potentials and challenges of Artificial Intelligence in supporting clinical diagnostics and remote assistance for the health and well-being of individuals
Berti A., Buongiorno R., Carloni G., Caudai C., Del Corso G., Germanese D., Pachetti E., Pascali M. A., Colantonio S.
Innovative technologies powered by Artificial Intelligence have the big potential to support new models of care delivery, disease prevention and quality of life promotion. The ultimate goal is a paradigm shift towards more personalized, accessible, effective, and sustainable care and health systems. Nevertheless, despite the advances in the field over the last years, the adoption and deployment of AI technologies remains limited in clinical practice and real-world settings. This paper summarizes the 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 for exploring both the potential of AI in health and well-being as well as the challenges to their uptake in real-world settings.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3486. Pisa, Italy, 29-30/05/2023
Berti A., Buongiorno R., Carloni G., Caudai C., Del Corso G., Germanese D., Pachetti E., Pascali M. A., Colantonio S.
Innovative technologies powered by Artificial Intelligence have the big potential to support new models of care delivery, disease prevention and quality of life promotion. The ultimate goal is a paradigm shift towards more personalized, accessible, effective, and sustainable care and health systems. Nevertheless, despite the advances in the field over the last years, the adoption and deployment of AI technologies remains limited in clinical practice and real-world settings. This paper summarizes the 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 for exploring both the potential of AI in health and well-being as well as the challenges to their uptake in real-world settings.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3486. Pisa, Italy, 29-30/05/2023
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ceur-ws.org | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
Complementing Hi-C information for 3D chromatin reconstruction by ChromStruct
Caudai C., Salerno E.
A multiscale method proposed elsewhere for reconstructing plausible 3D configurations of the chromatin in cell nuclei is recalled, based on the integration of contact data from Hi-C experiments and additional information coming from ChIP-seq, RNA-seq and ChIA-PET experiments. Provided that the additional data come from independent experiments, this kind of approach is supposed to leverage them to complement possibly noisy, biased or missing Hi-C records. When the different data sources are mutually concurrent, the resulting solutions are corroborated; otherwise, their validity would be weakened. Here, a problem of reliability arises, entailing an appropriate choice of the relative weights to be assigned to the different informational contributions. A series of experiments is presented that help to quantify the advantages and the limitations offered by this strategy. Whereas the advantages in accuracy are not always significant, the case of missing Hi-C data demonstrates the effectiveness of additional information in reconstructing the highly packed segments of the structure.Source: FRONTIERS IN BIOINFORMATICS, vol. 3
DOI: 10.3389/fbinf.2023.1287168
Metrics:
Caudai C., Salerno E.
A multiscale method proposed elsewhere for reconstructing plausible 3D configurations of the chromatin in cell nuclei is recalled, based on the integration of contact data from Hi-C experiments and additional information coming from ChIP-seq, RNA-seq and ChIA-PET experiments. Provided that the additional data come from independent experiments, this kind of approach is supposed to leverage them to complement possibly noisy, biased or missing Hi-C records. When the different data sources are mutually concurrent, the resulting solutions are corroborated; otherwise, their validity would be weakened. Here, a problem of reliability arises, entailing an appropriate choice of the relative weights to be assigned to the different informational contributions. A series of experiments is presented that help to quantify the advantages and the limitations offered by this strategy. Whereas the advantages in accuracy are not always significant, the case of missing Hi-C data demonstrates the effectiveness of additional information in reconstructing the highly packed segments of the structure.Source: FRONTIERS IN BIOINFORMATICS, vol. 3
DOI: 10.3389/fbinf.2023.1287168
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Frontiers in Bioinformatics | PubMed Central | CNR IRIS | Frontiers in Bioinformatics | www.frontiersin.org | GitHub | CNR IRIS
2022
Journal article
Open Access
Short-term abandonment versus mowing in a mediterranean-temperate meadow: effects on floristic composition, plant functionality, and soil properties - a case study
Vannucchi F, Lazzeri V, Rosellini I, Scatena M, Caudai C, Bretzel F
Hay meadows are secondary grasslands maintained by mowing, and their ecological importance resides in the inherent biodiversity and carbon stocking. We investigated the plant community and soil properties of a sub humid acid grassland near the Fucecchio marshes (Italy), managed as a hay meadow, mowed once a year, and not fertilized. Part of the meadow had been abandoned for three years. We analysed the soil properties (i.e., organic carbon and total nitrogen content, available phosphorus, pH, cation-exchange capacity, texture, and conductibility) and the plant community structure (composition, functionality, and species richness) of the two sides of the meadow (mowed and abandoned). Our aim was to highlight the changes in soil properties and vegetation community, and to find out to what extent abandonment can affect those dynamics. Our results showed that after short-term abandonment, soil pH, C and N increased; litter biomass and perennial forbs increased; and annual forbs decreased. New species colonising after abandonment, thus enriching the flora, may keep spreading and eventually hinder the growth of the specialists if mowing is not resumed. Certain valuable meadow habitats need constant human intervention to maintain their peculiar vegetation, most especially if they are a buffer zone in the proximity of natural protected areas.Source: AGRICULTURE, vol. 12 (issue 1)
DOI: 10.3390/agriculture12010078
Metrics:
Vannucchi F, Lazzeri V, Rosellini I, Scatena M, Caudai C, Bretzel F
Hay meadows are secondary grasslands maintained by mowing, and their ecological importance resides in the inherent biodiversity and carbon stocking. We investigated the plant community and soil properties of a sub humid acid grassland near the Fucecchio marshes (Italy), managed as a hay meadow, mowed once a year, and not fertilized. Part of the meadow had been abandoned for three years. We analysed the soil properties (i.e., organic carbon and total nitrogen content, available phosphorus, pH, cation-exchange capacity, texture, and conductibility) and the plant community structure (composition, functionality, and species richness) of the two sides of the meadow (mowed and abandoned). Our aim was to highlight the changes in soil properties and vegetation community, and to find out to what extent abandonment can affect those dynamics. Our results showed that after short-term abandonment, soil pH, C and N increased; litter biomass and perennial forbs increased; and annual forbs decreased. New species colonising after abandonment, thus enriching the flora, may keep spreading and eventually hinder the growth of the specialists if mowing is not resumed. Certain valuable meadow habitats need constant human intervention to maintain their peculiar vegetation, most especially if they are a buffer zone in the proximity of natural protected areas.Source: AGRICULTURE, vol. 12 (issue 1)
DOI: 10.3390/agriculture12010078
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CNR IRIS | ISTI Repository | www.mdpi.com | CNR IRIS
2022
Journal article
Open Access
Low productivity substrate leads to functional diversification of green roof plant assemblage
Vannucchi F, Buoncristiano A, Scatena M, Caudai C, Bretzel F
Green roofs are roof free spaces where living organisms can find an appropriate habitat to colonise. The establishment of plant species with different functionality can enhance biodiversity and provide ecosystem services. However, drought and nutrient availability can affect the plant development. The extensive green roof was set up in Pisa (Italy) in 2014, 12 modules of 10 cm depth were filled with three substrates composed of compost from municipal mixed waste, pelletised paper sludge, and commercial tephra product (Vulcaflor), as follows: Vulcaflor + compost, Vulcaflor + pellet + compost, and Vulcaflor + pellet, characterised by decreasing level of nitrogen content. The species planted in 2014 were chosen from the herbaceous spontaneous vegetation of urban and rural swards not often mowed, plus two sedum species. After the establishment phase, the green roof community was progressively dominated by Sedum species and other species were seeded in 2016. In 2018-19 the plant functional types and the community structure were monitored. Besides seasonal fluctuations, nitrogen shaped the composition of the community, and Sedum species showed high cover values in nitrogen-richer substrates. Annual forbs colonised the plots with a lower nitrogen content. In summer, the number of species drastically fell, and Sedum album was dominant in the three substrates. Seedling recruitment regenerated the community in the cooler season, increasing the diversity in the poor substrate. The scarcity of nitrogen led to the development of stress-tolerator annuals increasing the biodiversity in the rainy-cool season. Annual species constitute a transient seed bank which enables the system to regenerate when rain follows periods of heat and drought.Source: ECOLOGICAL ENGINEERING, vol. 176
DOI: 10.1016/j.ecoleng.2022.106547
Metrics:
Vannucchi F, Buoncristiano A, Scatena M, Caudai C, Bretzel F
Green roofs are roof free spaces where living organisms can find an appropriate habitat to colonise. The establishment of plant species with different functionality can enhance biodiversity and provide ecosystem services. However, drought and nutrient availability can affect the plant development. The extensive green roof was set up in Pisa (Italy) in 2014, 12 modules of 10 cm depth were filled with three substrates composed of compost from municipal mixed waste, pelletised paper sludge, and commercial tephra product (Vulcaflor), as follows: Vulcaflor + compost, Vulcaflor + pellet + compost, and Vulcaflor + pellet, characterised by decreasing level of nitrogen content. The species planted in 2014 were chosen from the herbaceous spontaneous vegetation of urban and rural swards not often mowed, plus two sedum species. After the establishment phase, the green roof community was progressively dominated by Sedum species and other species were seeded in 2016. In 2018-19 the plant functional types and the community structure were monitored. Besides seasonal fluctuations, nitrogen shaped the composition of the community, and Sedum species showed high cover values in nitrogen-richer substrates. Annual forbs colonised the plots with a lower nitrogen content. In summer, the number of species drastically fell, and Sedum album was dominant in the three substrates. Seedling recruitment regenerated the community in the cooler season, increasing the diversity in the poor substrate. The scarcity of nitrogen led to the development of stress-tolerator annuals increasing the biodiversity in the rainy-cool season. Annual species constitute a transient seed bank which enables the system to regenerate when rain follows periods of heat and drought.Source: ECOLOGICAL ENGINEERING, vol. 176
DOI: 10.1016/j.ecoleng.2022.106547
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ISTI Repository | CNR IRIS | CNR IRIS | CNR IRIS | www.sciencedirect.com