mlsanityData to 7 Oct 2026

IPMN Risk Assessment under Federated Learning Paradigm

arXiv 2411.05697 ↗First posted 8 Nov 2024eess.IV cs.DC cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 25 references that are in the map. First posted on arXiv 8 Nov 2024.

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20 of 25 references.

References · 20

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8 more
  1. 2022On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyondcited by 37
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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Pancreas Part Segmentation under Federated Learning Paradigmcited by 1
  2. 2025Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentationcited by 0