Precision and Robust Models on Healthcare Institution Federated Learning for Predicting HCC on Portal Venous CT Images.
Journal
IEEE journal of biomedical and health informatics
Journal Volume
28
Journal Issue
8
Pages
4674 - 4687
ISSN
2168-2208
Date Issued
2024-08
Author(s)
Hsiao, Chiu-Han
Lin, Frank Yeong-Sung
Sun, Tzu-Lung
Liao, Yen-Yen
Lai, Yu-Chun
Wu, Hung-Pei
Liu, Pin-Ruei
Xiao, Bo-Ren
Huang, Yennun
Abstract
Hepatocellular carcinoma (HCC), the most common type of liver cancer, poses significant challenges in detection and diagnosis. Medical imaging, especially computed tomography (CT), is pivotal in non-invasively identifying this disease, requiring substantial expertise for interpretation. This research introduces an innovative strategy that integrates two-dimensional (2D) and three-dimensional (3D) deep learning models within a federated learning (FL) framework for precise segmentation of liver and tumor regions in medical images. The study utilized 131 CT scans from the Liver Tumor Segmentation (LiTS) challenge and demonstrated the superior efficiency and accuracy of the proposed Hybrid-ResUNet model with a Dice score of 0.9433 and an AUC of 0.9965 compared to ResNet and EfficientNet models. This FL approach is beneficial for conducting large-scale clinical trials while safeguarding patient privacy across healthcare settings. It facilitates active engagement in problem-solving, data collection, model development, and refinement. The study also addresses data imbalances in the FL context, showing resilience and highlighting local models' robust performance. Future research will concentrate on refining federated learning algorithms and their incorporation into the continuous implementation and deployment (CI/CD) processes in AI system operations, emphasizing the dynamic involvement of clients. We recommend a collaborative human-AI endeavor to enhance feature extraction and knowledge transfer. These improvements are intended to boost equitable and efficient data collaboration across various sectors in practical scenarios, offering a crucial guide for forthcoming research in medical AI.
SDGs
Publisher
Institute of Electrical and Electronics Engineers Inc.
Type
journal article