Abstract
A chief challenge of deep learning in computer-aided diagnosis is to collect a large heterogeneous dataset from multiple hospitals for constructing a robust deep learning model, because of hospitals’ strict rules for sharing sensitive medical data. Federated learning (FL) allows collaborative learning among hospitals without sharing data to prevent a leakage of private information. FL, however, has issues of high computational demands and data shortage in local hospitals. To address these issues, we developed an FL framework coupled with a patch-wise deep learning model, a massive-training artificial neural network (MTANN), in tumor segmentation in CT. We performed experiments on the proposed MTANN-based federated tumor segmentation with a small-sized multisite liver tumor dataset. Our model achieved a Dice of 0.712 comparable to 0.723 by the gold-standard centralized training model, which was higher than 0.592 (P < 0.05) by the state-of-the-art Res-U-Net model. Furthermore, the proposed framework required a training time of 0.25 hours that was less than that of 5 hours by the Res-U-Net on a GPU server.
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Acknowledgements
This research is based on results obtained from a project commissioned by the NEDO. The authors are grateful to all members of the BMAI at Tokyo Institute of Technology for their valuable discussions.
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Yang, Y., Jin, Z., Suzuki, K. (2022). Federated Tumor Segmentation with Patch-Wise Deep Learning Model. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds) Machine Learning in Medical Imaging. MLMI 2022. Lecture Notes in Computer Science, vol 13583. Springer, Cham. https://doi.org/10.1007/978-3-031-21014-3_47
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