IPMN Risk Assessment Under Federated Learning Paradigm
Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Hongyi Pan, Ziliang Hong, Gorkem Durak, Elif Keles, Halil Ertugrul Aktas, Yavuz Taktak, Alpay Medetalibeyoglu, Zheyuan Zhang, Yury Velichko, Concetto Spampinato, Ivo Schoots, Marco J. Bruno, Pallavi Tiwari, Candice Bolan, Tamas Gonda, Frank Miller, Rajesh N. Keswani, Michael B. Wallace, Ziyue Xu, Ulas Bagci
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1144150618, G1266186184, Predicting Post-Covid Pulmonary Fibrosis with Explainable Deep Learning and Optimal Biomarker Discovery, G3015188539, G4139189009, G4162094395, G640430542, Radiologist-Centered Artificial Intelligence (RCAI) for Lung Cancer Screening and Diagnosis, G9000010256, G9000018199, G9000057517
OpenAlex ID: W4410296196