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2023
Journal article
Open Access
3D-Vision-transformer stacking ensemble for assessing prostate cancer aggressiveness from T2w images
Pachetti E., Colantonio S.
Vision transformers represent the cutting-edge topic in computer vision and are usually employed on two-dimensional data following a transfer learning approach. In this work, we propose a trained-from-scratch stacking ensemble of 3D-vision transformers to assess prostate cancer aggressiveness from T2-weighted images to help radiologists diagnose this disease without performing a biopsy. We trained 18 3D-vision transformers on T2-weighted axial acquisitions and combined them into two- and three-model stacking ensembles. We defined two metrics for measuring model prediction confidence, and we trained all the ensemble combinations according to a five-fold cross-validation, evaluating their accuracy, confidence in predictions, and calibration. In addition, we optimized the 18 base ViTs and compared the best-performing base and ensemble models by re-training them on a 100-sample bootstrapped training set and evaluating each model on the hold-out test set. We compared the two distributions by calculating the median and the 95% confidence interval and performing a Wilcoxon signed-rank test. The best-performing 3D-vision-transformer stacking ensemble provided state-of-the-art results in terms of area under the receiving operating curve (0.89 [0.61-1]) and exceeded the area under the precision-recall curve of the base model of 22% (p < 0.001). However, it resulted to be less confident in classifying the positive class.Source: Bioengineering (Basel) 10 (2023). doi:10.3390/bioengineering10091015
DOI: 10.3390/bioengineering10091015
Project(s): ProCAncer-I
Metrics:
Pachetti E., Colantonio S.
Vision transformers represent the cutting-edge topic in computer vision and are usually employed on two-dimensional data following a transfer learning approach. In this work, we propose a trained-from-scratch stacking ensemble of 3D-vision transformers to assess prostate cancer aggressiveness from T2-weighted images to help radiologists diagnose this disease without performing a biopsy. We trained 18 3D-vision transformers on T2-weighted axial acquisitions and combined them into two- and three-model stacking ensembles. We defined two metrics for measuring model prediction confidence, and we trained all the ensemble combinations according to a five-fold cross-validation, evaluating their accuracy, confidence in predictions, and calibration. In addition, we optimized the 18 base ViTs and compared the best-performing base and ensemble models by re-training them on a 100-sample bootstrapped training set and evaluating each model on the hold-out test set. We compared the two distributions by calculating the median and the 95% confidence interval and performing a Wilcoxon signed-rank test. The best-performing 3D-vision-transformer stacking ensemble provided state-of-the-art results in terms of area under the receiving operating curve (0.89 [0.61-1]) and exceeded the area under the precision-recall curve of the base model of 22% (p < 0.001). However, it resulted to be less confident in classifying the positive class.Source: Bioengineering (Basel) 10 (2023). doi:10.3390/bioengineering10091015
DOI: 10.3390/bioengineering10091015
Project(s): ProCAncer-I
Metrics:
See at:
ISTI Repository 
| www.mdpi.com | CNR ExploRA
2023
Conference article
Open Access
Causality-Driven One-Shot learning for prostate cancer grading from MRI
Carloni G., Pachetti E., Colantonio S.
In this paper, we present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutional neural network backbone and a causality-extractor module that extracts cause-effect relationships between feature maps that can inform the model on the appearance of a feature in one place of the image, given the presence of another feature within some other place of the image. To evaluate the effectiveness of our approach in low-data scenarios, we train our causality-driven architecture in a One-shot learning scheme, where we propose a new meta-learning procedure entailing meta-training and meta-testing tasks that are designed using related classes but at different levels of granularity. We conduct binary and multi-class classification experiments on a publicly available dataset of prostate MRI images. To validate the effectiveness of the proposed causality-driven module, we perform an ablation study and conduct qualitative assessments using class activation maps to highlight regions strongly influencing the network's decision-making process. Our findings show that causal relationships among features play a crucial role in enhancing the model's ability to discern relevant information and yielding more reliable and interpretable predictions. This would make it a promising approach for medical image classification tasks.Source: ICCV 2023 - International Conference on Computer Vision. Computer Vision for Automated Medical Diagnosis Workshop, Parigi, Francia, 02/10/2023
Carloni G., Pachetti E., Colantonio S.
In this paper, we present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutional neural network backbone and a causality-extractor module that extracts cause-effect relationships between feature maps that can inform the model on the appearance of a feature in one place of the image, given the presence of another feature within some other place of the image. To evaluate the effectiveness of our approach in low-data scenarios, we train our causality-driven architecture in a One-shot learning scheme, where we propose a new meta-learning procedure entailing meta-training and meta-testing tasks that are designed using related classes but at different levels of granularity. We conduct binary and multi-class classification experiments on a publicly available dataset of prostate MRI images. To validate the effectiveness of the proposed causality-driven module, we perform an ablation study and conduct qualitative assessments using class activation maps to highlight regions strongly influencing the network's decision-making process. Our findings show that causal relationships among features play a crucial role in enhancing the model's ability to discern relevant information and yielding more reliable and interpretable predictions. This would make it a promising approach for medical image classification tasks.Source: ICCV 2023 - International Conference on Computer Vision. Computer Vision for Automated Medical Diagnosis Workshop, Parigi, Francia, 02/10/2023
See at:
arxiv.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement
Xue Y., Du Y., Carloni G., Pachetti E., Jordan C., Tsaftaris S. A.
Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's function and condition in a non-invasive manner. Undersampling of the k-space is employed to reduce the scan duration, thus increasing patient comfort and reducing the risk of motion artefacts, at the cost of reduced image quality. In this challenge paper, we investigate the use of a convolutional recurrent neural network (CRNN) architecture to exploit temporal correlations in supervised cine cardiac MRI reconstruction. This is combined with a single-image super-resolution refinement module to improve single coil reconstruction by 4.4% in structural similarity and 3.9% in normalised mean square error compared to a plain CRNN implementation. We deploy a high-pass filter to our l1 loss to allow greater emphasis on high-frequency details which are missing in the original data. The proposed model demonstrates considerable enhancements compared to the baseline case and holds promising potential for further improving cardiac MRI reconstruction.Source: MICCAI 2023 - 26th International Conference on Medical Image Computing and Computer Assisted Intervention, Vancouver, Canada, 08-12/10/2023
Xue Y., Du Y., Carloni G., Pachetti E., Jordan C., Tsaftaris S. A.
Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's function and condition in a non-invasive manner. Undersampling of the k-space is employed to reduce the scan duration, thus increasing patient comfort and reducing the risk of motion artefacts, at the cost of reduced image quality. In this challenge paper, we investigate the use of a convolutional recurrent neural network (CRNN) architecture to exploit temporal correlations in supervised cine cardiac MRI reconstruction. This is combined with a single-image super-resolution refinement module to improve single coil reconstruction by 4.4% in structural similarity and 3.9% in normalised mean square error compared to a plain CRNN implementation. We deploy a high-pass filter to our l1 loss to allow greater emphasis on high-frequency details which are missing in the original data. The proposed model demonstrates considerable enhancements compared to the baseline case and holds promising potential for further improving cardiac MRI reconstruction.Source: MICCAI 2023 - 26th International Conference on Medical Image Computing and Computer Assisted Intervention, Vancouver, Canada, 08-12/10/2023
See at:
ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
A systematic review of few-shot learning in medical imaging
Pachetti E., Colantonio S.
The lack of annotated medical images limits the performance of deep learning models, which usually need large-scale labelled datasets. Few-shot learning techniques can reduce data scarcity issues and enhance medical image analysis, especially with meta-learning. This systematic review gives a comprehensive overview of few-shot learning in medical imaging. We searched the literature systematically and selected 80 relevant articles published from 2018 to 2023. We clustered the articles based on medical outcomes, such as tumour segmentation, disease classification, and image registration; anatomical structure investigated (i.e. heart, lung, etc.); and the meta-learning method used. For each cluster, we examined the papers' distributions and the results provided by the state-of-the-art. In addition, we identified a generic pipeline shared among all the studies. The review shows that few-shot learning can overcome data scarcity in most outcomes and that meta-learning is a popular choice to perform few-shot learning because it can adapt to new tasks with few labelled samples. In addition, following meta-learning, supervised learning and semi-supervised learning stand out as the predominant techniques employed to tackle few-shot learning challenges in medical imaging and also best performing. Lastly, we observed that the primary application areas predominantly encompass cardiac, pulmonary, and abdominal domains. This systematic review aims to inspire further research to improve medical image analysis and patient care.Source: Artificial intelligence in medicine (Print) (2023).
Pachetti E., Colantonio S.
The lack of annotated medical images limits the performance of deep learning models, which usually need large-scale labelled datasets. Few-shot learning techniques can reduce data scarcity issues and enhance medical image analysis, especially with meta-learning. This systematic review gives a comprehensive overview of few-shot learning in medical imaging. We searched the literature systematically and selected 80 relevant articles published from 2018 to 2023. We clustered the articles based on medical outcomes, such as tumour segmentation, disease classification, and image registration; anatomical structure investigated (i.e. heart, lung, etc.); and the meta-learning method used. For each cluster, we examined the papers' distributions and the results provided by the state-of-the-art. In addition, we identified a generic pipeline shared among all the studies. The review shows that few-shot learning can overcome data scarcity in most outcomes and that meta-learning is a popular choice to perform few-shot learning because it can adapt to new tasks with few labelled samples. In addition, following meta-learning, supervised learning and semi-supervised learning stand out as the predominant techniques employed to tackle few-shot learning challenges in medical imaging and also best performing. Lastly, we observed that the primary application areas predominantly encompass cardiac, pulmonary, and abdominal domains. This systematic review aims to inspire further research to improve medical image analysis and patient care.Source: Artificial intelligence in medicine (Print) (2023).
See at:
ISTI Repository | CNR ExploRA
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 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 settingsSource: Ital-IA 2023 - Italia Intelligenza Artificiale. Thematic Workshops of the 3rd CINI National Lab AIIS Conference on Artificial Intelligence - 2023, 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 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 settingsSource: Ital-IA 2023 - Italia Intelligenza Artificiale. Thematic Workshops of the 3rd CINI National Lab AIIS Conference on Artificial Intelligence - 2023, Pisa, Italy, 29-30/05/2023
Project(s): ProCAncer-I
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
AI trustworthiness in prostate cancer imaging: a look at algorithmic and system transparency
Colantonio S., Berti A., Buongiorno R., Del Corso G., Pachetti E., Pascali M. A., Kalantzopoulos C., Kalokyri V., Kondylakis H., Tachos N., Fotiadis D., Giannini V., Mazzetti S., Regge D., Papanikolaou N., Marias K., Tsiknakis M.
A responsible approach to artificial intelligence and machine learning technologies, grounded in sound scientific foundations, technical robustness, rigorous testing and validation, risk-based continuous monitoring and alignment with human values is imperative to guarantee their favourable impact and prevent any adverse effects they may have on individuals and communities. An essential aspect of responsible development is transparency, which constitutes a fundamental principle of the European approach towards artificial intelligence. Transparency can be achieved at different levels, such as data origin and use, system development, operation and usage. In this paper, we present the techniques implemented and delivered in the EU H2020 ProCAncer-I project to meet the transparency requirements at the different levels required.Source: IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, Malta, 7-9/12/2023
Project(s): ProCAncer-I
Colantonio S., Berti A., Buongiorno R., Del Corso G., Pachetti E., Pascali M. A., Kalantzopoulos C., Kalokyri V., Kondylakis H., Tachos N., Fotiadis D., Giannini V., Mazzetti S., Regge D., Papanikolaou N., Marias K., Tsiknakis M.
A responsible approach to artificial intelligence and machine learning technologies, grounded in sound scientific foundations, technical robustness, rigorous testing and validation, risk-based continuous monitoring and alignment with human values is imperative to guarantee their favourable impact and prevent any adverse effects they may have on individuals and communities. An essential aspect of responsible development is transparency, which constitutes a fundamental principle of the European approach towards artificial intelligence. Transparency can be achieved at different levels, such as data origin and use, system development, operation and usage. In this paper, we present the techniques implemented and delivered in the EU H2020 ProCAncer-I project to meet the transparency requirements at the different levels required.Source: IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, Malta, 7-9/12/2023
Project(s): ProCAncer-I
See at:
ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
Machine and deep learning prediction of prostate cancer aggressiveness using multiparametric MRI
E. Bertelli, L. Mercatelli, C. Marzi, E. Pachetti, M. Baccini, A. Barucci, S. Colantonio, L. Gherardini, L. Lattavo, M. A. Pascali, S. Agostini, V. Miele
Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score >= 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.Source: Frontiers in oncology 11 (2022). doi:10.3389/fonc.2021.802964
DOI: 10.3389/fonc.2021.802964
Project(s): ProCAncer-I
Metrics:
E. Bertelli, L. Mercatelli, C. Marzi, E. Pachetti, M. Baccini, A. Barucci, S. Colantonio, L. Gherardini, L. Lattavo, M. A. Pascali, S. Agostini, V. Miele
Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score >= 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.Source: Frontiers in oncology 11 (2022). doi:10.3389/fonc.2021.802964
DOI: 10.3389/fonc.2021.802964
Project(s): ProCAncer-I
Metrics:
See at:
ISTI Repository | www.frontiersin.org | CNR ExploRA
2022
Contribution to book
Open Access
On the effectiveness of 3D vision transformers for the prediction of prostate cancer aggressiveness
Pachetti E., Colantonio S., Pascali M. A.
Prostate cancer is the most frequent male neoplasm in European men. To date, the gold standard for determining the aggressiveness of this tumor is the biopsy, an invasive and uncomfortable procedure. Before the biopsy, physicians recommend an investigation by multiparametric magnetic resonance imaging, which may serve the radiologist to gather an initial assessment of the tumor. The study presented in this work aims to investigate the role of Vision Transformers in predicting prostate cancer aggressiveness based only on imaging data. We designed a 3D Vision Transformer able to process volumetric scans, and we optimized it on the ProstateX-2 challenge dataset by training it from scratch. As a term of comparison, we also designed a 3D Convolutional Neural Network, and we optimized it in a similar fashion. The results obtained by our preliminary investigations show that Vision Transformers, even without extensive optimization and customization, can ensure an improved performance with respect to Convolutional Neural Networks and might be comparable with other more fine-tuned solutions.Source: Image Analysis and Processing, edited by Mazzeo P.L., Frontoni E., Sclaroff S., Distante C., pp. 317–328. Switzerland: Springer International Publishing, 2022
DOI: 10.1007/978-3-031-13324-4_27
Project(s): ProCAncer-I
Metrics:
Pachetti E., Colantonio S., Pascali M. A.
Prostate cancer is the most frequent male neoplasm in European men. To date, the gold standard for determining the aggressiveness of this tumor is the biopsy, an invasive and uncomfortable procedure. Before the biopsy, physicians recommend an investigation by multiparametric magnetic resonance imaging, which may serve the radiologist to gather an initial assessment of the tumor. The study presented in this work aims to investigate the role of Vision Transformers in predicting prostate cancer aggressiveness based only on imaging data. We designed a 3D Vision Transformer able to process volumetric scans, and we optimized it on the ProstateX-2 challenge dataset by training it from scratch. As a term of comparison, we also designed a 3D Convolutional Neural Network, and we optimized it in a similar fashion. The results obtained by our preliminary investigations show that Vision Transformers, even without extensive optimization and customization, can ensure an improved performance with respect to Convolutional Neural Networks and might be comparable with other more fine-tuned solutions.Source: Image Analysis and Processing, edited by Mazzeo P.L., Frontoni E., Sclaroff S., Distante C., pp. 317–328. Switzerland: Springer International Publishing, 2022
DOI: 10.1007/978-3-031-13324-4_27
Project(s): ProCAncer-I
Metrics:
See at:
ISTI Repository | link.springer.com 
| CNR ExploRA
2022
Conference article
Open Access
Data models for an imaging bio-bank for colorectal, prostate and gastric cancer: the NAVIGATOR project
Berti A., Carloni G., Colantonio S., Pascali M. A., Manghi P., Pagano P., Buongiorno R., Pachetti E., Caudai C., Di Gangi D., Carlini E., Falaschi Z., Ciarrocchi E., Neri E., Bertelli E., Miele V., Carpi R., Bagnacci G., Di Meglio N., Mazzei M. A., Barucci A.
Researchers nowadays may take advantage of broad collections of medical data to develop personalized medicine solutions. Imaging bio-banks play a fundamental role, in this regard, by serving as organized repositories of medical images associated with imaging biomarkers. In this context, the NAVIGATOR Project aims to advance colorectal, prostate, and gastric oncology translational research by leveraging quantitative imaging and multi-omics analyses. As Project's core, an imaging bio-bank is being designed and implemented in a web-accessible Virtual Research Environment (VRE). The VRE serves to extract the imaging biomarkers and further process them within prediction algorithms. In our work, we present the realization of the data models for the three cancer use-cases of the Project. First, we carried out an extensive requirements analysis to fulfill the necessities of the clinical partners involved in the Project. Then, we designed three separate data models utilizing entity-relationship diagrams. We found diagrams' modeling for colorectal and prostate cancers to be more straightforward, while gastric cancer required a higher level of complexity. Future developments of this work would include designing a common data model following the Observational Medical Outcomes Partnership Standards. Indeed, a common data model would standardize the logical infrastructure of data models and make the bio-bank easily interoperable with other bio-banks.Source: BHI '22 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Ioannina, Greece, 27-30/09/2022
DOI: 10.1109/bhi56158.2022.9926910
Metrics:
Berti A., Carloni G., Colantonio S., Pascali M. A., Manghi P., Pagano P., Buongiorno R., Pachetti E., Caudai C., Di Gangi D., Carlini E., Falaschi Z., Ciarrocchi E., Neri E., Bertelli E., Miele V., Carpi R., Bagnacci G., Di Meglio N., Mazzei M. A., Barucci A.
Researchers nowadays may take advantage of broad collections of medical data to develop personalized medicine solutions. Imaging bio-banks play a fundamental role, in this regard, by serving as organized repositories of medical images associated with imaging biomarkers. In this context, the NAVIGATOR Project aims to advance colorectal, prostate, and gastric oncology translational research by leveraging quantitative imaging and multi-omics analyses. As Project's core, an imaging bio-bank is being designed and implemented in a web-accessible Virtual Research Environment (VRE). The VRE serves to extract the imaging biomarkers and further process them within prediction algorithms. In our work, we present the realization of the data models for the three cancer use-cases of the Project. First, we carried out an extensive requirements analysis to fulfill the necessities of the clinical partners involved in the Project. Then, we designed three separate data models utilizing entity-relationship diagrams. We found diagrams' modeling for colorectal and prostate cancers to be more straightforward, while gastric cancer required a higher level of complexity. Future developments of this work would include designing a common data model following the Observational Medical Outcomes Partnership Standards. Indeed, a common data model would standardize the logical infrastructure of data models and make the bio-bank easily interoperable with other bio-banks.Source: BHI '22 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Ioannina, Greece, 27-30/09/2022
DOI: 10.1109/bhi56158.2022.9926910
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2022
Other
Open Access
SI-Lab annual research report 2021
Righi M., Leone G. R., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Berti A., Bruno A., Buongiorno R., Carloni G., Conti F., Germanese D., Ignesti G., Matarese F., Omrani A., Pachetti E., Papini O., Benassi A., Bertini G., Coltelli P., Tarabella L., Straface S., Salvetti O., Moroni D.
The Signal & Images Laboratory 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 2021.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/003
Metrics:
Righi M., Leone G. R., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Berti A., Bruno A., Buongiorno R., Carloni G., Conti F., Germanese D., Ignesti G., Matarese F., Omrani A., Pachetti E., Papini O., Benassi A., Bertini G., Coltelli P., Tarabella L., Straface S., Salvetti O., Moroni D.
The Signal & Images Laboratory 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 2021.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/003
Metrics:
See at:
ISTI Repository | CNR ExploRA
2021
Other
Open Access
Technical report on the development and interpretation of convolutional neural networks for the classification of multiparametric MRI images on unbalanced datasets. Case study: prostate cancer
Pachetti E., Colantonio S.
This report summarized the activities carried out to define, train and validate Deep Learning models for the classification of medical imaging data. The issue of unbalanced datasets was faced by applying some data augmentation techniques, based on transformation of the original images. Such techniques were compared to verify their impact in a frame where object morphology is relevant. Multimodal deep learning models were defined to exploit the information contained in heterogeneous imaging data and cope with data distribution imbalance. To verify the inner functioning of the deep learning models, the LIME algorithm was applied, thus checking that the regions that contribute to the classification were the real meaningful ones. The case study used to was the categorization of prostate cancer aggressiveness based on Magnetic Resonance Imaging (MRI) data. The aggressiveness was determined, as a ground truth, via tissue biopsy and expressed with a score from 2 to 10 known as Gleason Score, which is obtained as the sum of two values, each one from 1 to 5, associated with the two most common patterns in the tumor tissue histological sample.Source: ISTI Technical Report, ISTI-2021-TR/005, pp.1–16, 2021
Pachetti E., Colantonio S.
This report summarized the activities carried out to define, train and validate Deep Learning models for the classification of medical imaging data. The issue of unbalanced datasets was faced by applying some data augmentation techniques, based on transformation of the original images. Such techniques were compared to verify their impact in a frame where object morphology is relevant. Multimodal deep learning models were defined to exploit the information contained in heterogeneous imaging data and cope with data distribution imbalance. To verify the inner functioning of the deep learning models, the LIME algorithm was applied, thus checking that the regions that contribute to the classification were the real meaningful ones. The case study used to was the categorization of prostate cancer aggressiveness based on Magnetic Resonance Imaging (MRI) data. The aggressiveness was determined, as a ground truth, via tissue biopsy and expressed with a score from 2 to 10 known as Gleason Score, which is obtained as the sum of two values, each one from 1 to 5, associated with the two most common patterns in the tumor tissue histological sample.Source: ISTI Technical Report, ISTI-2021-TR/005, pp.1–16, 2021
See at:
ISTI Repository | CNR ExploRA