Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification utilizing a comprehensive pancreas MRI dataset. This dataset includes 652 T1-weighted and 655 T2-weighted MRI images, accompanied by corresponding IPMN risk scores from 7 leading medical institutions, making it the largest and most diverse dataset for IPMN classification to date. We assess the performance of DenseNet-121 in both centralized and federated settings for training on distributed data. Our results demonstrate that the federated learning approach achieves high classification accuracy comparable to centralized learning while ensuring data privacy across institutions. This work marks a significant advancement in collaborative IPMN classification, facilitating secure and high-accuracy model training across multiple centers.
@article{arxiv.2411.05697,
title = {IPMN Risk Assessment under Federated Learning Paradigm},
author = {Hongyi Pan and Ziliang Hong and Gorkem Durak and Elif Keles and Halil Ertugrul Aktas and Yavuz Taktak and Alpay Medetalibeyoglu and Zheyuan Zhang and Yury Velichko and Concetto Spampinato and Ivo Schoots and Marco J. Bruno and Pallavi Tiwari and Candice Bolan and Tamas Gonda and Frank Miller and Rajesh N. Keswani and Michael B. Wallace and Ziyue Xu and Ulas Bagci},
journal= {arXiv preprint arXiv:2411.05697},
year = {2025}
}