Typology
operator:
and / or
Language
operator:
and / or
Date
operator:
and / or
Rights
operator:
and / or
2022
Journal article
Open Access
In-vivo range verification analysis with in-beam PET data for patients treated with proton therapy at CNAO
Moglioni M., Christine Kraan A., Baroni G., Battistoni G., Belcari N., Berti A., Carra P., Cerello P., Ciocca M., De Gregorio A., De Simoni M., Del Sarto D., Donetti M., Dong Y., Embriaco A., Fantacci M. E., Ferrero V., Fiorina E., Fischetti M., Franciosini G., Giraudo G., Laruina F., Maestri D., Magi M., Magro G., Malekzadeh E., Marafini M., Mattei I., Mazzoni E., Mereu P., Mirandola A., Morrocchi M., Muraro S., Orlandi E., Patera V., Pennazio F., Pullia M., Retico A., Rivetti A., Da Rocha Rolo M. D., Rosso V., Sarti A., Schiavi A., Sciubba A., Sportelli G., Tampellini S., Toppi M., Traini G., Trigilio A., Valle S. M., Valvo F., Vischioni B., Vitolo V., Wheadon R., Bisogni M. G.
Morphological changes that may arise through a treatment course are probably one of the most significant sources of range uncertainty in proton therapy. Non- invasive in-vivo treatment monitoring is useful to increase treatment quality. The INSIDE in-beam Positron Emission Tomography (PET) scanner performs in-vivo range monitoring in proton and carbon therapy treatments at the National Center of Oncological Hadrontherapy (CNAO). It is currently in a clinical trial (ID: NCT03662373) and has acquired in-beam PET data during the treatment of various patients. In this work we analyze the in-beam PET (IB-PET) data of eight patients treated with proton therapy at CNAO. The goal of the analysis is twofold. First, we assess the level of experimental fluctuations in inter-fractional range differences (sensitivity) of the INSIDE PET system by studying patients without morphological changes. Second, we use the obtained results to see whether we can observe anomalously large range variations in patients where morphological changes have occurred. The sensitivity of the INSIDE IB-PET scanner was quantified as the standard deviation of the range difference distributions observed for six patients that did not show morphological changes. Inter- fractional range variations with respect to a reference distribution were estimated using the Most-Likely-Shift (MLS) method. To establish the efficacy of this method, we made a comparison with the Beam's Eye View (BEV) method. For patients showing no morphological changes in the control CT the average range variation standard deviation was found to be 2.5 mm with the MLS method and 2.3 mm with the BEV method. On the other hand, for patients where some small anatomical changes occurred, we found larger standard deviation values. In these patients we evaluated where anomalous range differences were found and compared them with the CT. We found that the identified regions were mostly in agreement with the morphological changes seen in the CT scan.Source: Frontiers in oncology (2022). doi:10.3389/fonc.2022.929949
DOI: 10.3389/fonc.2022.929949
Metrics:
Moglioni M., Christine Kraan A., Baroni G., Battistoni G., Belcari N., Berti A., Carra P., Cerello P., Ciocca M., De Gregorio A., De Simoni M., Del Sarto D., Donetti M., Dong Y., Embriaco A., Fantacci M. E., Ferrero V., Fiorina E., Fischetti M., Franciosini G., Giraudo G., Laruina F., Maestri D., Magi M., Magro G., Malekzadeh E., Marafini M., Mattei I., Mazzoni E., Mereu P., Mirandola A., Morrocchi M., Muraro S., Orlandi E., Patera V., Pennazio F., Pullia M., Retico A., Rivetti A., Da Rocha Rolo M. D., Rosso V., Sarti A., Schiavi A., Sciubba A., Sportelli G., Tampellini S., Toppi M., Traini G., Trigilio A., Valle S. M., Valvo F., Vischioni B., Vitolo V., Wheadon R., Bisogni M. G.
Morphological changes that may arise through a treatment course are probably one of the most significant sources of range uncertainty in proton therapy. Non- invasive in-vivo treatment monitoring is useful to increase treatment quality. The INSIDE in-beam Positron Emission Tomography (PET) scanner performs in-vivo range monitoring in proton and carbon therapy treatments at the National Center of Oncological Hadrontherapy (CNAO). It is currently in a clinical trial (ID: NCT03662373) and has acquired in-beam PET data during the treatment of various patients. In this work we analyze the in-beam PET (IB-PET) data of eight patients treated with proton therapy at CNAO. The goal of the analysis is twofold. First, we assess the level of experimental fluctuations in inter-fractional range differences (sensitivity) of the INSIDE PET system by studying patients without morphological changes. Second, we use the obtained results to see whether we can observe anomalously large range variations in patients where morphological changes have occurred. The sensitivity of the INSIDE IB-PET scanner was quantified as the standard deviation of the range difference distributions observed for six patients that did not show morphological changes. Inter- fractional range variations with respect to a reference distribution were estimated using the Most-Likely-Shift (MLS) method. To establish the efficacy of this method, we made a comparison with the Beam's Eye View (BEV) method. For patients showing no morphological changes in the control CT the average range variation standard deviation was found to be 2.5 mm with the MLS method and 2.3 mm with the BEV method. On the other hand, for patients where some small anatomical changes occurred, we found larger standard deviation values. In these patients we evaluated where anomalous range differences were found and compared them with the CT. We found that the identified regions were mostly in agreement with the morphological changes seen in the CT scan.Source: Frontiers in oncology (2022). doi:10.3389/fonc.2022.929949
DOI: 10.3389/fonc.2022.929949
Metrics:
See at:
ISTI Repository 
| www.frontiersin.org | CNR ExploRA
2023
Journal article
Open Access
Analysis methods for in-beam PET images in proton therapy treatment verification: a comparison based on Monte Carlo simulations
Moglioni M., Kraan A. C., Berti A., Carra P., Cerello P., Ciocca M., Ferrero V., Fiorina E., Mazzoni E., Morrocchi M., Pennazio F., Retico A., Rosso V., Sportelli G., Vitolo V., Bisogni G.
Background and purpose: in-beam Positron Emission Tomography (PET) is one of the modalities that can be used for in-vivo non-invasive treatment monitoring in proton therapy. PET distributions obtained during various treatment sessions can be compared in order to identify regions that have anatomical changes. The purpose of this work is to test and compare different analysis methods in the context of inter-fractional PET image comparison for proton treatment verification. Methods: for our study we used the FLUKA Monte Carlo code and artificially generated CT scans to simulate in-beam PET distributions at different stages during proton therapy treatment. We compared the Beam-Eye-View method, the Most-Likely-Shift method, the Voxel-Based-Morphology method and the gamma evaluation method to compare PET images at the start of treatment, and after a few weeks of treatment. The results were compared to the CT scan. Results and conclusions: three-dimensional methods like VBM and gamma are preferred above two-dimensional methods like MLS and BEV if much statistics is available, since the these methods allow to identify the regions with anomalous activity. The VBM approach has as disadvantage that a larger number of MC simulations is needed. The gamma analysis has the disadvantage that no clinical indication exist on tolerance criteria. In terms of calculation time, the BEV and MLS method are preferred. We recommend to use the four methods together, in order to best identify the location and cause of the activity changes.Source: Journal of instrumentation 18 (2023). doi:10.1088/1748-0221/18/01/C01001
DOI: 10.1088/1748-0221/18/01/c01001
DOI: 10.48550/arxiv.2210.04584
Metrics:
Moglioni M., Kraan A. C., Berti A., Carra P., Cerello P., Ciocca M., Ferrero V., Fiorina E., Mazzoni E., Morrocchi M., Pennazio F., Retico A., Rosso V., Sportelli G., Vitolo V., Bisogni G.
Background and purpose: in-beam Positron Emission Tomography (PET) is one of the modalities that can be used for in-vivo non-invasive treatment monitoring in proton therapy. PET distributions obtained during various treatment sessions can be compared in order to identify regions that have anatomical changes. The purpose of this work is to test and compare different analysis methods in the context of inter-fractional PET image comparison for proton treatment verification. Methods: for our study we used the FLUKA Monte Carlo code and artificially generated CT scans to simulate in-beam PET distributions at different stages during proton therapy treatment. We compared the Beam-Eye-View method, the Most-Likely-Shift method, the Voxel-Based-Morphology method and the gamma evaluation method to compare PET images at the start of treatment, and after a few weeks of treatment. The results were compared to the CT scan. Results and conclusions: three-dimensional methods like VBM and gamma are preferred above two-dimensional methods like MLS and BEV if much statistics is available, since the these methods allow to identify the regions with anomalous activity. The VBM approach has as disadvantage that a larger number of MC simulations is needed. The gamma analysis has the disadvantage that no clinical indication exist on tolerance criteria. In terms of calculation time, the BEV and MLS method are preferred. We recommend to use the four methods together, in order to best identify the location and cause of the activity changes.Source: Journal of instrumentation 18 (2023). doi:10.1088/1748-0221/18/01/C01001
DOI: 10.1088/1748-0221/18/01/c01001
DOI: 10.48550/arxiv.2210.04584
Metrics:
See at:
arXiv.org e-Print Archive | ISTI Repository | Journal of Instrumentation 
| doi.org | Archivio Istituzionale | iopscience.iop.org | CNR ExploRA
2023
Conference article
Open Access
On the applicability of prototypical part learning in medical images: breast masses classification using ProtoPNet
Carloni G., Berti A., Iacconi C., Pascali M. A., Colantonio S.
Deep learning models have become state-of-the-art in many areas, ranging from computer vision to agriculture research. However, concerns have been raised with respect to the transparency of their decisions, especially in the image domain. In this regard, Explainable Artificial Intelligence has been gaining popularity in recent years. The ProtoPNet model, which breaks down an image into prototypes and uses evidence gathered from the prototypes to classify an image, represents an appealing approach. Still, questions regarding its effectiveness arise when the application domain changes from real-world natural images to gray-scale medical images. This work explores the applicability of prototypical part learning in medical imaging by experimenting with ProtoPNet on a breast masses classification task. The two considered aspects were the classification capabilities and the validity of explanations. We looked for the optimal model's hyperparameter configuration via a random search. We trained the model in a five-fold CV supervised framework, with mammogram images cropped around the lesions and ground-truth labels of benign/malignant masses. Then, we compared the performance metrics of ProtoPNet to that of the corresponding base architecture, which was ResNet18, trained under the same framework. In addition, an experienced radiologist provided a clinical viewpoint on the quality of the learned prototypes, the patch activations, and the global explanations. We achieved a Recall of 0.769 and an area under the receiver operating characteristic curve of 0.719 in our experiments. Even though our findings are non-optimal for entering the clinical practice yet, the radiologist found ProtoPNet's explanations very intuitive, reporting a high level of satisfaction. Therefore, we believe that prototypical part learning offers a reasonable and promising trade-off between classification performance and the quality of the related explanation.Source: ICPR 2022 - International Conference on Pattern Recognition - ICPR 2022 International Workshops and Challenges, pp. 539–557, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37660-3_38
Metrics:
Carloni G., Berti A., Iacconi C., Pascali M. A., Colantonio S.
Deep learning models have become state-of-the-art in many areas, ranging from computer vision to agriculture research. However, concerns have been raised with respect to the transparency of their decisions, especially in the image domain. In this regard, Explainable Artificial Intelligence has been gaining popularity in recent years. The ProtoPNet model, which breaks down an image into prototypes and uses evidence gathered from the prototypes to classify an image, represents an appealing approach. Still, questions regarding its effectiveness arise when the application domain changes from real-world natural images to gray-scale medical images. This work explores the applicability of prototypical part learning in medical imaging by experimenting with ProtoPNet on a breast masses classification task. The two considered aspects were the classification capabilities and the validity of explanations. We looked for the optimal model's hyperparameter configuration via a random search. We trained the model in a five-fold CV supervised framework, with mammogram images cropped around the lesions and ground-truth labels of benign/malignant masses. Then, we compared the performance metrics of ProtoPNet to that of the corresponding base architecture, which was ResNet18, trained under the same framework. In addition, an experienced radiologist provided a clinical viewpoint on the quality of the learned prototypes, the patch activations, and the global explanations. We achieved a Recall of 0.769 and an area under the receiver operating characteristic curve of 0.719 in our experiments. Even though our findings are non-optimal for entering the clinical practice yet, the radiologist found ProtoPNet's explanations very intuitive, reporting a high level of satisfaction. Therefore, we believe that prototypical part learning offers a reasonable and promising trade-off between classification performance and the quality of the related explanation.Source: ICPR 2022 - International Conference on Pattern Recognition - ICPR 2022 International Workshops and Challenges, pp. 539–557, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37660-3_38
Metrics:
See at:
ISTI Repository | link.springer.com | CNR ExploRA
2023
Journal article
Open Access
The role of causality in explainable artificial intelligence
Carloni G., Berti A., Colantonio S.
Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science, even though the underlying concepts of causation and explanation share common ancient roots. This is further enforced by the lack of review works jointly covering these two fields. In this paper, we investigate the literature to try to understand how and to what extent causality and XAI are intertwined. More precisely, we seek to uncover what kinds of relationships exist between the two concepts and how one can benefit from them, for instance, in building trust in AI systems. As a result, three main perspectives are identified. In the first one, the lack of causality is seen as one of the major limitations of current AI and XAI approaches, and the "optimal" form of explanations is investigated. The second is a pragmatic perspective and considers XAI as a tool to foster scientific exploration for causal inquiry, via the identification of pursue-worthy experimental manipulations. Finally, the third perspective supports the idea that causality is propaedeutic to XAI in three possible manners: exploiting concepts borrowed from causality to support or improve XAI, utilizing counterfactuals for explainability, and considering accessing a causal model as explaining itself. To complement our analysis, we also provide relevant software solutions used to automate causal tasks. We believe our work provides a unified view of the two fields of causality and XAI by highlighting potential domain bridges and uncovering possible limitations.Source: Wiley interdisciplinary reviews. Data mining and knowledge discovery (2023).
Project(s): ProCAncer-I
Carloni G., Berti A., Colantonio S.
Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science, even though the underlying concepts of causation and explanation share common ancient roots. This is further enforced by the lack of review works jointly covering these two fields. In this paper, we investigate the literature to try to understand how and to what extent causality and XAI are intertwined. More precisely, we seek to uncover what kinds of relationships exist between the two concepts and how one can benefit from them, for instance, in building trust in AI systems. As a result, three main perspectives are identified. In the first one, the lack of causality is seen as one of the major limitations of current AI and XAI approaches, and the "optimal" form of explanations is investigated. The second is a pragmatic perspective and considers XAI as a tool to foster scientific exploration for causal inquiry, via the identification of pursue-worthy experimental manipulations. Finally, the third perspective supports the idea that causality is propaedeutic to XAI in three possible manners: exploiting concepts borrowed from causality to support or improve XAI, utilizing counterfactuals for explainability, and considering accessing a causal model as explaining itself. To complement our analysis, we also provide relevant software solutions used to automate causal tasks. We believe our work provides a unified view of the two fields of causality and XAI by highlighting potential domain bridges and uncovering possible limitations.Source: Wiley interdisciplinary reviews. Data mining and knowledge discovery (2023).
Project(s): ProCAncer-I
See at:
arxiv.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
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
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
Report
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