IPMN Risk Assessment Under Federated Learning Paradigm

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.

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Publication Details

Published
2025-04-14
DOI
https://doi.org/10.1109/isbi60581.2025.10980733
Citations
2
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
2.97
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article

IPMN Risk Assessment Under Federated Learning Paradigm

Hongyi Pan, Frank Miller, Rajesh N. Keswani, Michael B. Wallace et al.
2 citations
Network Security and Intrusion Detection
2.97
article

IPMN Risk Assessment Under Federated Learning Paradigm

Hongyi Pan, Frank Miller, Rajesh N. Keswani, Michael B. Wallace, Ulaş Bağcı, Görkem Durak, Ziyue Xu, Marco J. Bruno, Pallavi Tiwari, Candice W. Bolan, Tamas A. Gonda, Yury Velichko, Elif Keleş, Ivo G. Schoots, Concetto Spampinato, Yavuz Taktak, Zheyuan Zhang, Ziliang Hong, Alpay Medetalibeyoğlu, Halil Ertugrul Aktas
article en
2 citations

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.

Northwestern University (US), University of Wisconsin–Madison (US), Jacksonville College (US), Erasmus MC (NL), University of Catania (IT), Nvidia (United States) (US), Mayo Clinic in Florida (US), New York University (US), Istanbul University (TR)
Openalex Percentile: Top 9%
Network Security and Intrusion Detection
2.97
DOISourceOpenAlex
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