IPMN Risk Assessment under Federated Learning Paradigm
This addresses the need for secure and accurate IPMN risk assessment across medical institutions, though it is incremental as it applies an existing federated learning method to a new medical dataset.
The study tackled IPMN classification using a federated learning framework on a large multi-center pancreas MRI dataset, achieving high accuracy comparable to centralized training while preserving data privacy.
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.