Publications

This page shows all the publications that have been produced by the PRE-ACT project so far.

Journal publications

G. Bologna, "Transferring CNN features maps to ensembles of explainable neural networks", Information 14(2): 89, MDPI, 2023.  
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
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. 
Y. Liang, Y. Zhou, R. Houben, K. Verhoeven, S. Rivera, L. Boersma, "A systematic review and meta-analysis of risk factors influencing patient-reported arm symptoms post-breast cancer treatment: Accounting for radiotherapy impact", The Breast 78 (2024): 103812.
G. Bologna, J.M. Boutay, BD. Boquete, Q. Leblanc., D. Köprülü, & L. Pfeiffer, "Fidex and FidexGlo: From Local Explanations to Global Explanations of Deep Models". Algorithms, 18(3), 120, 2025.
S. Nikoloutsopoulos, I. Koutsopoulos, M.K. Titsias, "Personalized Federated Learning with Exact Stochastic Gradient Descent", Applied Intelligence 55(17), 1123, 2025
Y. Papageorgiou, Y. Thomas, R. Khalili, I. Koutsopoulos, "Split Federated Learning Architectures for High-Accuracy and Low-Delay Model Training", arXiv preprint arXiv:2603.08687, 2026

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 
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
F.P. Nerini, P. Bajardi, A. Panisson, “Value is in the Eye of the Beholder: A Framework for an Equitable Graph Data Evaluation”, ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2024, 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, accepted to the European Cancer Summit 2024
F. Charalampakos, T. Tsouparopoulos, I. Koutsopoulos, "Joint Explainability-Performance Optimization With Surrogate Models for AI-Driven Edge Services", accepted to IEEE International Conference on Machine Learning for Communication and Networking (IEEE ICMLCN), Barcelona, Spain, 2025
T. Tsouparopoulos, I. Koutsopoulos, "Explainability and Continual Learning meet Federated Learning at the Network Edge", accepted to International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt), Linköping, Sweden, 2025
Y. Papageorgiou, Y. Thomas, A. Filippakopoulos, R. Khalili, and I. Koutsopoulos, "Collaborative Split Federated Learning with Parallel Training and Aggregation", accepted to the International Conference on Artificial Intelligence and Applications and Innovations (AIAI), Limassol, Cyprus, 2025
A. Babey, J.M. Boutay, R. Marquis, D.B. Costa, G. Bologna, M. Graf and C.A. Peña-Reyes, C. A, “HES-XPLAIN - An Open Platform for Accelerating the Development of eXplainable AI Systems", accepted to the AI days, Geneva, Switzerland, 2025
J-M. Boutay, Q. Leblanc, B.D. Boquete, D. Köprülü, L. Pfeiffer, G. Bologna, "Explaining CNN Classifications Using Small Patches", accepted to the first International Workshop on Advanced Neuro-Symbolic Applications (ANSyA), Co-located with the European Conference of AI (ECAI) 2025, Bologna, Italy, 2025
A. Ferrara, F. Cozzi, A. Perotti, A. Panisson, F. Bonchi, "Size-adaptive Hypothesis Testing for Fairness", accepted to the thirty-ninth annual conference on Neural Information Processing Systems (NeurIPS 2025), San Diego, USA, 2025

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, 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
Y. Liang, P. Bajardi, G. Bologna, e.a., PRE-ACT: Prediction of Radiotherapy side Effects using explainable AI for patient Communication and Treatment modification at European Cancer Summit 2024
T. Rattay, K. Verhoeven, M. Bergeaud, G. Bologna, G. Cortellessa, A. Dekker, F. Fracasso, M. Joore, I. Koutsopoulos, Y. Liang, A. Panisson, A. Perotti, B. Ramaekers, C. Roumen, J. van Soest, H. Stobart, C. Talbot, A. Webb, and S. Rivera. Poster (P435): PRE-ACT-01: The Prediction of Radiotherapy side Effects using explainable AI for patient Communication and Treatment modification-01 Trial at 19^th St.Gallen International Breast Cancer Conference 2025
F. Cozzi, A. Panisson, A. Perotti, A. Ferrara, G. Bologna, S. Rivera, M. Bergeaud, C. Gaudin, G. Auzac, T. Sarrade, I. Vaz Luis, T. Rattay, A. Romita, K. Verhoeven, Y. Liang, J. Rainbird, M. Balia, B. Ramaekers, W. Witlox, C. Roumen, G. Cortellessa, F. Fracasso, I. Koutsopoulos , C. Talbot, "Impact of Missing Data on AI Fairness for Breast Cancer Radiotherapy: Insights from the PRE-ACT Project", at the ESTRO 2025
Y. Papageorgiou, T. Tsouparopoulos, F. Charalambakos, I. Koutsopoulos, "Multilevel Split Federated Learning: The PRE-ACT case", at the 4th MC/WG meeting of the COST Action AtheroNET CA21153
T. Holly, B.M Sugden, W.J.A. Witlox, B.L.T. Ramaekers, Multi-country cost analyses of artificial intelligence diagnostics in radiotherapy, at CAPHRI research day 2025
T. Rattay, K. Verhoeven, M. Bergeaud, G. Bologna, G. Cortellessa, A. Dekker, F. Fracasso, M. Joore, I. Koutsopoulos, Y. Liang, A. Panisson, A. Perotti, B. Ramaekers, C. Roumen, J. van Soest, H. Stobart, C. Talbot, A. Webb, S. Rivera, PRE-ACT Study Consortium, PRE-ACT-01: The Prediction of Radiotherapy side Effects using explainable AI for patient Communication and Treatment modification-01 Trial at The UK Interdisciplinary Breast Cancer Symposium 2026