|Title:||Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning||Authors:||Wang, Pochuan
Roth, Holger R.
Chen, Po Ting
|Keywords:||Federated learning | Neural architecture search | Pancreas segmentation||Issue Date:||1-Jan-2020||Journal Volume:||12444 LNCS||Source:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||Abstract:||
© 2020, Springer Nature Switzerland AG. The performance of deep learning based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image analysis field. However, unlike photography images, it is hard to generate centralized databases to collect medical images because of numerous technical, legal, and privacy issues. In this work, we study the use of federated learning between two institutions in a real-world setting to collaboratively train a model without sharing the raw data across national boundaries. We quantitatively compare the segmentation models obtained with federated learning and local training alone. Our experimental results show that federated learning models have higher generalizability than standalone training.
|Appears in Collections:||醫學院附設醫院 (臺大醫院)|
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