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2021
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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.
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.
See at:
CNR IRIS 
| ISTI Repository | CNR IRIS 
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, vol. 10 (issue 9)
Project(s): ProCAncer-I
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, vol. 10 (issue 9)
Project(s): ProCAncer-I
See at:
CNR IRIS | ISTI Repository | www.mdpi.com | CNR IRIS
2024
Contribution to book
Open Access
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement
Xue Y, Du Y, Carloni G, Pachetti E, Jordan C, Tsaftaris Sa
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 missingin 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: LECTURE NOTES IN COMPUTER SCIENCE, vol. 14507, pp. 421-432
Xue Y, Du Y, Carloni G, Pachetti E, Jordan C, Tsaftaris Sa
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 missingin 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: LECTURE NOTES IN COMPUTER SCIENCE, vol. 14507, pp. 421-432
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2024
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, vol. 156
Project(s): ProCAncer-I, NAVIGATOR
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, vol. 156
Project(s): ProCAncer-I
, NAVIGATOR
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
Seeing more with less: meta-learning and diffusion models for tumor characterization in low-data settings
Pachetti E., Colantonio S.
While deep learning excels in many areas, its application in medicine is hindered by limited data, which restricts model generalizability. Few-shot learning has emerged as a potential solution to this problem. In this work, we leverage the strengths of meta-learning, the primary framework for few-shot learning, along with diffusion-based generative models to enhance few-shot learning capabilities. We propose a novel method that jointly trains a diffusion model and a feature extractor in an episodic-based manner. The diffusion model learns conditional generation based on each episode’s support samples. After updating its parameters, it generates additional support samples for each class. The augmented support set is used to train a feature extractor within a prototypical meta-learning framework. Notably, we propose a weighted prototype computation based on the distance between each generated sample and the original class prototype, i.e., derived solely from the original support samples. Evaluations on two tumor characterization tasks (prostate cancer aggressiveness and breast cancer malignity assessment) demonstrate our approach’s effectiveness in improving prototype representation and boosting classification performance. Find our code at: https://github.com/evapachetti/meta_diffusion.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15199, pp. 48-58. Marrakesh, Morocco, 06/10/2024
Project(s): ProCAncer-I, An Imaging Biobank to Precisely Prevent and Predict cancer, and facilitate the Participation of oncologic patients to Diagnosis and Treatment
Pachetti E., Colantonio S.
While deep learning excels in many areas, its application in medicine is hindered by limited data, which restricts model generalizability. Few-shot learning has emerged as a potential solution to this problem. In this work, we leverage the strengths of meta-learning, the primary framework for few-shot learning, along with diffusion-based generative models to enhance few-shot learning capabilities. We propose a novel method that jointly trains a diffusion model and a feature extractor in an episodic-based manner. The diffusion model learns conditional generation based on each episode’s support samples. After updating its parameters, it generates additional support samples for each class. The augmented support set is used to train a feature extractor within a prototypical meta-learning framework. Notably, we propose a weighted prototype computation based on the distance between each generated sample and the original class prototype, i.e., derived solely from the original support samples. Evaluations on two tumor characterization tasks (prostate cancer aggressiveness and breast cancer malignity assessment) demonstrate our approach’s effectiveness in improving prototype representation and boosting classification performance. Find our code at: https://github.com/evapachetti/meta_diffusion.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15199, pp. 48-58. Marrakesh, Morocco, 06/10/2024
Project(s): ProCAncer-I
, An Imaging Biobank to Precisely Prevent and Predict cancer, and facilitate the Participation of oncologic patients to Diagnosis and Treatment
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Boosting few-shot learning with disentangled self-supervised learning and meta-learning for medical image classification
Pachetti E., Tsaftaris S. A., Colantonio S.
Background and objective: Employing deep learning models in critical domainssuch as medical imaging poses challenges associated with the limitedavailability of training data. We present a strategy for improving theperformance and generalization capabilities of models trained in low-dataregimes. Methods: The proposed method starts with a pre-training phase, wherefeatures learned in a self-supervised learning setting are disentangled toimprove the robustness of the representations for downstream tasks. We thenintroduce a meta-fine-tuning step, leveraging related classes betweenmeta-training and meta-testing phases but varying the granularity level. Thisapproach aims to enhance the model's generalization capabilities by exposing itto more challenging classification tasks during meta-training and evaluating iton easier tasks but holding greater clinical relevance during meta-testing. Wedemonstrate the effectiveness of the proposed approach through a series ofexperiments exploring several backbones, as well as diverse pre-training andfine-tuning schemes, on two distinct medical tasks, i.e., classification ofprostate cancer aggressiveness from MRI data and classification of breastcancer malignity from microscopic images. Results: Our results indicate thatthe proposed approach consistently yields superior performance w.r.t. ablationexperiments, maintaining competitiveness even when a distribution shift betweentraining and evaluation data occurs. Conclusion: Extensive experimentsdemonstrate the effectiveness and wide applicability of the proposed approach.We hope that this work will add another solution to the arsenal of addressinglearning issues in data-scarce imaging domains.Source: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE UPDATE
Pachetti E., Tsaftaris S. A., Colantonio S.
Background and objective: Employing deep learning models in critical domainssuch as medical imaging poses challenges associated with the limitedavailability of training data. We present a strategy for improving theperformance and generalization capabilities of models trained in low-dataregimes. Methods: The proposed method starts with a pre-training phase, wherefeatures learned in a self-supervised learning setting are disentangled toimprove the robustness of the representations for downstream tasks. We thenintroduce a meta-fine-tuning step, leveraging related classes betweenmeta-training and meta-testing phases but varying the granularity level. Thisapproach aims to enhance the model's generalization capabilities by exposing itto more challenging classification tasks during meta-training and evaluating iton easier tasks but holding greater clinical relevance during meta-testing. Wedemonstrate the effectiveness of the proposed approach through a series ofexperiments exploring several backbones, as well as diverse pre-training andfine-tuning schemes, on two distinct medical tasks, i.e., classification ofprostate cancer aggressiveness from MRI data and classification of breastcancer malignity from microscopic images. Results: Our results indicate thatthe proposed approach consistently yields superior performance w.r.t. ablationexperiments, maintaining competitiveness even when a distribution shift betweentraining and evaluation data occurs. Conclusion: Extensive experimentsdemonstrate the effectiveness and wide applicability of the proposed approach.We hope that this work will add another solution to the arsenal of addressinglearning issues in data-scarce imaging domains.Source: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE UPDATE
2024
Conference article
Open Access
Hallucinating for diagnosing: one-shot medical image classification leveraging score-based generative models
Pachetti E., Colantonio S.
Deep learning models in data-scarce domains, such as medical imaging, often suffer from poor performance due to the challenges of acquiring large amounts of labeled data. Few-shot learning offers a promising solution to this problem. This work proposes a novel framework to jointly train a score-based generative model for high-quality sample hallucination and a meta-learning framework for one-shot classification. We evaluate our approach on MRI scans of prostate cancer, aiming to classify tumors based on severity. Our preliminary experiments demonstrate promising results, indicating the efficacy of our proposed method in improving classification performance. Future work will involve further analysis using a diverse set of score models and prototypical meta-learning techniques, as well as evaluation of the effectiveness of our framework in other medical imaging tasks.
Pachetti E., Colantonio S.
Deep learning models in data-scarce domains, such as medical imaging, often suffer from poor performance due to the challenges of acquiring large amounts of labeled data. Few-shot learning offers a promising solution to this problem. This work proposes a novel framework to jointly train a score-based generative model for high-quality sample hallucination and a meta-learning framework for one-shot classification. We evaluate our approach on MRI scans of prostate cancer, aiming to classify tumors based on severity. Our preliminary experiments demonstrate promising results, indicating the efficacy of our proposed method in improving classification performance. Future work will involve further analysis using a diverse set of score models and prototypical meta-learning techniques, as well as evaluation of the effectiveness of our framework in other medical imaging tasks.
See at:
2024.midl.io | CNR IRIS | CNR IRIS
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, Ma 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, vol. 11 (issue 802964)
Project(s): ProCAncer-I
E Bertelli, L Mercatelli, C Marzi, E Pachetti, M Baccini, A Barucci, S Colantonio, L Gherardini, L Lattavo, Ma 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, vol. 11 (issue 802964)
Project(s): ProCAncer-I
See at:
CNR IRIS | ISTI Repository | www.frontiersin.org | CNR IRIS
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 Ma
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.Project(s): ProCAncer-I
Pachetti E, Colantonio S, Pascali Ma
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.Project(s): ProCAncer-I
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
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.
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.
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Few-shot conditional learning: automatic and reliable device classification for medical test equipment
Pachetti E., Del Corso G., Bardelli S., Colantonio S.
: The limited availability of specialized image databases (particularly in hospitals, where tools vary between providers) makes it difficult to train deep learning models. This paper presents a few-shot learning methodology that uses a pre-trained ResNet integrated with an encoder as a backbone to encode conditional shape information for the classification of neonatal resuscitation equipment from less than 100 natural images. The model is also strengthened by incorporating a reliability score, which enriches the prediction with an estimation of classification reliability. The model, whose performance is cross-validated, reached a median accuracy performance of over 99% (and a lower limit of 73.4% for the least accurate model/fold) using only 87 meta-training images. During the test phase on complex natural images, performance was slightly degraded due to a sub-optimal segmentation strategy (FastSAM) required to maintain the real-time inference phase (median accuracy 87.25%). This methodology proves to be excellent for applying complex classification models to contexts (such as neonatal resuscitation) that are not available in public databases. Improvements to the automatic segmentation strategy prior to the extraction of conditional information will allow a natural application in simulation and hospital settings.Source: JOURNAL OF IMAGING, vol. 10 (issue 7)
Pachetti E., Del Corso G., Bardelli S., Colantonio S.
: The limited availability of specialized image databases (particularly in hospitals, where tools vary between providers) makes it difficult to train deep learning models. This paper presents a few-shot learning methodology that uses a pre-trained ResNet integrated with an encoder as a backbone to encode conditional shape information for the classification of neonatal resuscitation equipment from less than 100 natural images. The model is also strengthened by incorporating a reliability score, which enriches the prediction with an estimation of classification reliability. The model, whose performance is cross-validated, reached a median accuracy performance of over 99% (and a lower limit of 73.4% for the least accurate model/fold) using only 87 meta-training images. During the test phase on complex natural images, performance was slightly degraded due to a sub-optimal segmentation strategy (FastSAM) required to maintain the real-time inference phase (median accuracy 87.25%). This methodology proves to be excellent for applying complex classification models to contexts (such as neonatal resuscitation) that are not available in public databases. Improvements to the automatic segmentation strategy prior to the extraction of conditional information will allow a natural application in simulation and hospital settings.Source: JOURNAL OF IMAGING, vol. 10 (issue 7)
See at:
CNR IRIS | www.mdpi.com | CNR IRIS
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 Ma, 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.Project(s): ProCAncer-I
Colantonio S, Berti A, Buongiorno R, Del Corso G, Pachetti E, Pascali Ma, 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.Project(s): ProCAncer-I
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences
Mylona E., Zaridis D. I., Kalantzopoulos C., Tachos N. S., Regge D., Papanikolaou N., Tsiknakis M., Marias K., Marquez R., Henne T., Saillant C., Mora J. M., Pastor A. J., Agraniotis D., Pollalis C., Giavri Z., Hernandez W., Correia J., Bridge C., Kalpathy-Cramer J., Carloni G., Berti A., Germanese D., Del Corso G., Pachetti E., Pascali M. A., Colantonio S., Napolitano V., Maimone G., Cappello G., Mazzetti S., Giannini V., García-Martí G., Jacobs T., Doran S., Ribeiro A., Vit S., Emsley R., Koh D. M., Georgios G., Vasilis K., Slidevska K., Untanas A., Briediene R., Usinskiene J., Vilanova J. C., Karcaaltincaba M., Atak F., Karaosmanoglu A. D., Özmen M., Akata D., Nan, Mendola V., Tumminello L., Aringhieri G., Neri E., Marfil M., Navarro S., Ribas G., Cerdá-Alberich L., Martí-Bonmatí L., Futterer J., Twilt J. J., Saha A., De Rooij M., Huisman H., Chambel M., Rodrigues N., Rodrigues A. C., Verde A. C., De Almeida J. G., Dimitriadis A., Kalliatakis G., Trivizakis E., Kalokyri V., Sfakianakis S., Fotiadis D. I.
Objectives: Radiomics-based analyses encompass multiple steps, leading to ambiguity regarding the optimal approaches for enhancing model performance. This study compares the effect of several feature selection methods, machine learning (ML) classifiers, and sources of radiomic features, on models' performance for the diagnosis of clinically significant prostate cancer (csPCa) from bi-parametric MRI. Methods: Two multi-centric datasets, with 465 and 204 patients each, were used to extract 1246 radiomic features per patient and MRI sequence. Ten feature selection methods, such as Boruta, mRMRe, ReliefF, recursive feature elimination (RFE), random forest (RF) variable importance, L1-lasso, etc., four ML classifiers, namely SVM, RF, LASSO, and boosted generalized linear model (GLM), and three sets of radiomics features, derived from T2w images, ADC maps, and their combination, were used to develop predictive models of csPCa. Their performance was evaluated in a nested cross-validation and externally, using seven performance metrics. Results: In total, 480 models were developed. In nested cross-validation, the best model combined Boruta with Boosted GLM (AUC = 0.71, F1 = 0.76). In external validation, the best model combined L1-lasso with boosted GLM (AUC = 0.71, F1 = 0.47). Overall, Boruta, RFE, L1-lasso, and RF variable importance were the top-performing feature selection methods, while the choice of ML classifier didn't significantly affect the results. The ADC-derived features showed the highest discriminatory power with T2w-derived features being less informative, while their combination did not lead to improved performance. Conclusion: The choice of feature selection method and the source of radiomic features have a profound effect on the models' performance for csPCa diagnosis. Critical relevance statement: This work may guide future radiomic research, paving the way for the development of more effective and reliable radiomic models; not only for advancing prostate cancer diagnostic strategies, but also for informing broader applications of radiomics in different medical contexts. Key points: Radiomics is a growing field that can still be optimized. Feature selection method impacts radiomics models' performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. ADC-derived radiomic features yield more robust models compared to T2w-derived radiomic features.Source: INSIGHTS INTO IMAGING, vol. 15 (issue 1)
Project(s): ProCAncer-I
Mylona E., Zaridis D. I., Kalantzopoulos C., Tachos N. S., Regge D., Papanikolaou N., Tsiknakis M., Marias K., Marquez R., Henne T., Saillant C., Mora J. M., Pastor A. J., Agraniotis D., Pollalis C., Giavri Z., Hernandez W., Correia J., Bridge C., Kalpathy-Cramer J., Carloni G., Berti A., Germanese D., Del Corso G., Pachetti E., Pascali M. A., Colantonio S., Napolitano V., Maimone G., Cappello G., Mazzetti S., Giannini V., García-Martí G., Jacobs T., Doran S., Ribeiro A., Vit S., Emsley R., Koh D. M., Georgios G., Vasilis K., Slidevska K., Untanas A., Briediene R., Usinskiene J., Vilanova J. C., Karcaaltincaba M., Atak F., Karaosmanoglu A. D., Özmen M., Akata D., Nan, Mendola V., Tumminello L., Aringhieri G., Neri E., Marfil M., Navarro S., Ribas G., Cerdá-Alberich L., Martí-Bonmatí L., Futterer J., Twilt J. J., Saha A., De Rooij M., Huisman H., Chambel M., Rodrigues N., Rodrigues A. C., Verde A. C., De Almeida J. G., Dimitriadis A., Kalliatakis G., Trivizakis E., Kalokyri V., Sfakianakis S., Fotiadis D. I.
Objectives: Radiomics-based analyses encompass multiple steps, leading to ambiguity regarding the optimal approaches for enhancing model performance. This study compares the effect of several feature selection methods, machine learning (ML) classifiers, and sources of radiomic features, on models' performance for the diagnosis of clinically significant prostate cancer (csPCa) from bi-parametric MRI. Methods: Two multi-centric datasets, with 465 and 204 patients each, were used to extract 1246 radiomic features per patient and MRI sequence. Ten feature selection methods, such as Boruta, mRMRe, ReliefF, recursive feature elimination (RFE), random forest (RF) variable importance, L1-lasso, etc., four ML classifiers, namely SVM, RF, LASSO, and boosted generalized linear model (GLM), and three sets of radiomics features, derived from T2w images, ADC maps, and their combination, were used to develop predictive models of csPCa. Their performance was evaluated in a nested cross-validation and externally, using seven performance metrics. Results: In total, 480 models were developed. In nested cross-validation, the best model combined Boruta with Boosted GLM (AUC = 0.71, F1 = 0.76). In external validation, the best model combined L1-lasso with boosted GLM (AUC = 0.71, F1 = 0.47). Overall, Boruta, RFE, L1-lasso, and RF variable importance were the top-performing feature selection methods, while the choice of ML classifier didn't significantly affect the results. The ADC-derived features showed the highest discriminatory power with T2w-derived features being less informative, while their combination did not lead to improved performance. Conclusion: The choice of feature selection method and the source of radiomic features have a profound effect on the models' performance for csPCa diagnosis. Critical relevance statement: This work may guide future radiomic research, paving the way for the development of more effective and reliable radiomic models; not only for advancing prostate cancer diagnostic strategies, but also for informing broader applications of radiomics in different medical contexts. Key points: Radiomics is a growing field that can still be optimized. Feature selection method impacts radiomics models' performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. ADC-derived radiomic features yield more robust models compared to T2w-derived radiomic features.Source: INSIGHTS INTO IMAGING, vol. 15 (issue 1)
Project(s): ProCAncer-I
See at:
CNR IRIS | insightsimaging.springeropen.com | CNR IRIS
2024
Conference article
Restricted
Radiomics-based reliable predictions of side effects after radiotherapy for prostate cancer
Del Corso G., Pachetti E., Buongiorno R., Rodrigues A. C., Germanese D., Pascali M. A., Almeida J., Rodrigues N., Tsiknakis M., Papanikolaou N., Regge D., Marias K., Consortium Procancer-I, Colantonio S.
This work offers insight into the effectiveness of probabilistic models, specifically those based on ensemble approximations, in predicting adverse side effects following radiotherapy for prostate cancer. We trained a random forest model on radiomic features from 134 T2-weighted Magnetic Resonance (MRI) images of the prostate gland to identify patients experiencing acute or chronic rectal and urinary toxicity (AU-ROC ranging from 61.4% for endorectal coil acquisitions to 70.8% for the full dataset). We evaluated the reliability of the predictions using an ensemble approximation of simplified random forests obtained by an adaptive procedure of random subsampling of the training data. We used this reliability score to define a not-confident class and then recompute performance metrics more in accordance with a probabilistic approach. The outcomes we obtained (up to 7.9% increase in accuracy) indicate the approximated probabilistic models pledge more reliable predictions, thus being suitable for further investigation.Project(s): ProCAncer-I
Del Corso G., Pachetti E., Buongiorno R., Rodrigues A. C., Germanese D., Pascali M. A., Almeida J., Rodrigues N., Tsiknakis M., Papanikolaou N., Regge D., Marias K., Consortium Procancer-I, Colantonio S.
This work offers insight into the effectiveness of probabilistic models, specifically those based on ensemble approximations, in predicting adverse side effects following radiotherapy for prostate cancer. We trained a random forest model on radiomic features from 134 T2-weighted Magnetic Resonance (MRI) images of the prostate gland to identify patients experiencing acute or chronic rectal and urinary toxicity (AU-ROC ranging from 61.4% for endorectal coil acquisitions to 70.8% for the full dataset). We evaluated the reliability of the predictions using an ensemble approximation of simplified random forests obtained by an adaptive procedure of random subsampling of the training data. We used this reliability score to define a not-confident class and then recompute performance metrics more in accordance with a probabilistic approach. The outcomes we obtained (up to 7.9% increase in accuracy) indicate the approximated probabilistic models pledge more reliable predictions, thus being suitable for further investigation.Project(s): ProCAncer-I
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS
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 Ma, 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 Ma, 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.
Berti A, Carloni G, Colantonio S, Pascali Ma, 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 Ma, 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.
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | 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
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
From Covid-19 detection to cancer grading: how medical-AI is boosting clinical diagnostics and may improve treatment
Berti A., Buongiorno R., Carloni G., Caudai C., Conti F., Del Corso G., Germanese D., Moroni D., Pachetti E., Pascali M. A., Colantonio S.
The integration of artificial intelligence (AI) into medical imaging has guided an era of transformation in healthcare. This paper presents the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in medical imaging. From the convolutional neural network-based segmentation of Covid-19 lung patterns to the radiomic signature for benign/malignant breast nodule discrimination, to the automatic grading of prostate cancer, this work highlights the paradigm shift that AI has brought to medical imaging, revolutionizing diagnosis and patient care.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 336-341. Naples, Italy, 29-30/05/2024
Berti A., Buongiorno R., Carloni G., Caudai C., Conti F., Del Corso G., Germanese D., Moroni D., Pachetti E., Pascali M. A., Colantonio S.
The integration of artificial intelligence (AI) into medical imaging has guided an era of transformation in healthcare. This paper presents the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in medical imaging. From the convolutional neural network-based segmentation of Covid-19 lung patterns to the radiomic signature for benign/malignant breast nodule discrimination, to the automatic grading of prostate cancer, this work highlights the paradigm shift that AI has brought to medical imaging, revolutionizing diagnosis and patient care.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 336-341. Naples, Italy, 29-30/05/2024
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
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.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.DOI: 10.32079/isti-ar-2022/003
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS