Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal Volume
12444 LNCS
ISBN
9783030605476
Date Issued
2020-01-01
Author(s)
Wang, Pochuan
Shen, Chen
Roth, Holger R.
Yang, Dong
Xu, Daguang
Oda, Masahiro
Misawa, Kazunari
Kensaku, Mori
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.
Subjects
Federated learning | Neural architecture search | Pancreas segmentation
Type
conference paper