Publications
This page shows all the publications that have been produced by the PRE-ACT project so far.
Journal publications
- J. M. Górriz, I. Álvarez-Illán, A. Álvarez-Marquina, J.E. Arco, M. Atzmueller, F. Ballarini, E. Barakova, G. Bologna, P. Bonomini, G. Castellanos-Dominguez, D. Castillo-Barnes, et al., “Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends”, Information Fusion 100: 101945, Elsevier, 2023
- G. Bologna, "Transferring CNN features maps to ensembles of explainable neural networks", Information 14(2): 89, MDPI, 2024.
- C. Panigutti, A. Beretta, D. Fadda, F. Giannotti, D. Pedreschi, A. Perotti and S. Rinzivillo, "Co-design of human-centered, explainable AI for clinical decision support", ACM Transactions on Interactive Intelligent Systems 13(4): 1-35, 2023.
Paper publications in conferences/workshops
- F. Charalampakos, T. Tsouparopoulos, I. Papageorgiou, G. Bologna, A. Panisson, A. Perotti and I. Koutsopoulos, "Research Challenges in Trustworthy Artificial Intelligence and Computing for Health: The Case of the PRE-ACT project", in Proceedings of the Joint European Conference on Networks and Communications & 6G Summit, IEEE, Gothenburg, Sweden, 2023
- I. Koutsopoulos, “PRE-ACT: Prediction of Radiotherapy Side Effects using Explainable AI for Patient Communication and Treatment Modification”, in Proceedings. Of the Leading and Management in the Digital Era (LMDE), (extended abstract), Syros, Greece, 2023
- T. Tsouparopoulos and I. Koutsopoulos, "On improving accuracy in Federated Learning using GANs-based pre-training and Ensemble Learning", in Proceedings. of the 1st Workshop on Advancements in Federated Learning (WAFL) of the ECML/PKDD, Turin, Italy, 2023
- F. Charalampakos and I. Koutsopoulos, “Exploring Multi-Task Learning for Explainability", in Proceedings of the 3rd International Workshop on Explainable and Interpretable Machine Learning (XI-ML) of the 26th European Conference on Artificial Intelligence (ECAI), Krakow, Poland, 2023
- G. Bologna, J-M. Boutay, Q. Leblanc and D. Boquete, "Fidex: an Algorithm for the Explainability of Ensembles and SVMs", Accepted to the International Conference on the Interplay between Natural and Artificial Computation (IWINAC’24), 2024
- G. B. Bordes and A. Perotti, "Auditing Fairness and Explainability in Chest X-Ray Image Classifiers", In Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART), 2024
Posters
- T. Rattay, G. Bologna, A. Bombezin-Domino, G. Cortellessa, A. Dekker, F. Fracasso, M. Joore, A. Pannison, A. Perotti, B. L.T. Ramaekers, S. Rivera, A. Romita, C. Roumen, J. van Soest, H. Stobart, F. Tohidinezhad, A. Traverso, K. Verhoeven, A. J. Webb, I. Koutsopoulos and C. J. Talbot, “Development of an ai prediction model for arm lymphoedema following breast cancer surgery and radiotherapy”, European Journal of Surgical Oncology 50, 2024
- T. Rattay, G. Bologna, A. Bombezin-Domino, G. Cortellessa, A. Dekker, F. Fracasso, M. Joore, A. Pannison, A. Perotti, B.L.T. Ramaekers, S. Rivera, A. Romita, C. Roumen, J. van Soest, A. Traverso, F. Tohidinezhad, K. Verhoeven, A.J. Webb, I. Koutsopoulos, C.J. Talbot, Poster Spotlight: “Development of an explainable AI prediction model for arm lymphoedema following breast cancer surgery and radiotherapy”, at the 14th European Breast Cancer Conference, Milan, 2024
- C. Roumen , J. Rainbird , K. Verhoeven , G. Bologna , A. Bombezin-Domino , T. Rattay , J. van Soest, A. Dekker , M. Joore , A. Pannison, A. Perotti, B.L.T. Ramaekers , S. Rivera A. Romita , A. Traverso, A.J. Webb, I. Koutsopoulos, C.J. Talbot, G. Cortellessa, F. Fracasso, 252 (PB-068) Poster: "Breast cancer patients’ communication needs and wishes for an explainable Artificial Intelligence prediction model for lymphedema" , European Journal of Cancer, 2024
- Y. Liang, J. Rainbird, G. Cortellessa, M. Balia, G. Bologna, I. Koutsopoulos, A. Panisson, A. Perotti, B.L.T. Ramaekers, T. Rattay, S. Rivera, A. Romita, C. Roumen, C. Talbot, K. Verhoeven, M. Bergeaud and F. Fracasso, Physicians’ views on Explainable Artificial Intelligence (XAI) models to predict the risk of toxicity following breast radiotherapy, at the ASTRO 2024
- K. Verhoeven, T. Brion, W. R. Green, M. Balia, A. Webb, T. Rattay, Y. Liang, L. G. Assia, I. Hafsa, S. Romdhani, R. Iandolo, A. Bombezin-Domino, B. K. K. Teo, R. McBeth, I. Koutsopoulos, C. Talbot, N. Paragios, and S. Rivera, Multi-Institutional qualitative evaluation of automatic and manual segmentations of organs at risk on PRE ACT breast cancer cohorts at the ASTRO 2024
- Yuqin Liang, Paolo Bajardi, Guido Bologna, e.a. PRE-ACT: Prediction of Radiotherapy side Effects using explainable AI for patient Communication and Treatment modification at European Cancer Summit 2024