| A deep look into radiomics C Scapicchio, M Gabelloni, A Barucci, D Cioni, L Saba, E Neri La radiologia medica 126 (10), 1296-1311, 2021 | 293 | 2021 |
| Bridging gaps between images and data: a systematic update on imaging biobanks M Gabelloni, L Faggioni, R Borgheresi, G Restante, J Shortrede, ... European radiology, 1-14, 2022 | 22 | 2022 |
| Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine V Brancato, G Esposito, L Coppola, C Cavaliere, P Mirabelli, ... Journal of Translational Medicine 22 (1), 136, 2024 | 20 | 2024 |
| DICOM-MIABIS integration model for biobanks: a use case of the EU PRIMAGE project C Scapicchio, M Gabelloni, SM Forte, LC Alberich, L Faggioni, ... European Radiology Experimental 5, 1-12, 2021 | 14 | 2021 |
| Convolutional neural networks for breast density classification: performance and explanation insights F Lizzi, C Scapicchio, F Laruina, A Retico, ME Fantacci Applied Sciences 12 (1), 148, 2021 | 13 | 2021 |
| A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia C Scapicchio, A Chincarini, E Ballante, L Berta, E Bicci, C Bortolotto, ... European Radiology Experimental 7 (1), 18, 2023 | 9 | 2023 |
| Explainability of a CNN for breast density assessment C Scapicchio, F Lizzi, ME Fantacci Il nuovo cimento C 44, 1, 2021 | 3 | 2021 |
| EXPLAINING THE BEHAVIOUR OF A CONVOLUTIONAL NEURAL NETWORK FOR BREAST DENSITY ASSESSMENT C Scapicchio, A Retico, F Lizzi, ME Fantacci Physica Medica 104, 42-42, 2022 | 1 | 2022 |
| SC10. 04 EVALUATION OF REPEATABILITY AND ROBUSTNESS OF CT-DERIVED RADIOMIC FEATURES USING A CUSTOM PHANTOM MI Tenerani, M Imbriani, F Lizzi, S Pallotta, M Quattrocchi, A Retico, ... Physica Medica 125, 103472, 2024 | | 2024 |
| A Multi-input Deep Learning Model to Classify COVID-19 Pneumonia Severity from Imaging and Clinical Data F Lizzi, F Brero, M Evelina Fantacci, A Lascialfari, G Paternò, I Postuma, ... International Work-Conference on Bioinformatics and Biomedical Engineering …, 2024 | | 2024 |
| Investigation of a potential upstream harmonization based on image appearance matching to improve radiomics features robustness: a phantom study C Scapicchio, M Imbriani, F Lizzi, M Quattrocchi, A Retico, S Saponaro, ... Biomedical Physics & Engineering Express 10 (4), 045006, 2024 | | 2024 |
| Implementation and optimization of explainable and trustworthy Artificial Intelligence algorithms for the analysis of radiological images C SCAPICCHIO | | 2024 |
| Explainability Applied to a Deep-Learning Based Algorithm for Lung Nodule Segmentation CSMEF Arman Zafaranchi, Francesca Lizzi, Alessandra Retico Proceedings of the 1st International Conference on Explainable AI for Neural …, 2024 | | 2024 |
| EVALUATION OF REPEATABILITY AND ROBUSTNESS OF CT-DERIVED RADIOMIC FEATURES USING A CUSTOM PHANTOM MEF M.I. Tenerani , M. Imbriani , F. Lizzi , S. Pallotta , M. Quattrocchi ... Physica Medica, 2024 | | 2024 |
| Integration and Optimization of XNAT-Based Platforms for the Management of Heterogeneous and Multicenter Data in Biomedical Research AR Camilla Scapicchio, Silvia Arezzini, Maria Evelina Fantacci, Antonino ... roceedings of the 13th International Conference on Data Science, Technology …, 2024 | | 2024 |
| Characterization and Quantification of Image Quality in CT Imaging Systems: A Phantom Study C Scapicchio, M Imbriani, F Lizzi, M Quattrocchi, A Retico, S Saponaro, ... Proceedings of the 17th International Joint Conference on Biomedical …, 2024 | | 2024 |
| Acknowledgment to the Reviewers of Sensors in 2022 Sensors Editorial Office Sensors 23 (3), 1179, 2023 | | 2023 |
| COVID-19 SEVERITY PREDICTION BASED ON RADIOMIC FEATURES EXTRACTED FROM LUNG CT SCANS USING THE LUNGQUANT SEGMENTATION SOFTWARE C Scapicchio, E Ballante, A Benfante, L Berta, C Bortolotto, F Brero, ... Physica Medica 115, 2023 | | 2023 |
| Acknowledgment to the Reviewers of Sensors in 2022 AD Prasad, AN Sharkawy, AKMM Rahman, AR Akl, AP Zhang, A Elsaid, ... Sensors 23, 1179, 2023 | | 2023 |
| Integration of a Deep Learning-Based Module for the Quantification of Imaging Features into the Filling-in Process of the Radiological Structured Report. C Scapicchio, E Ballante, F Brero, RF Cabini, A Chincarini, ME Fantacci, ... HEALTHINF, 663-670, 2023 | | 2023 |
