Poster: A Distributed Deep Reinforcement Learning System for Medical Image Segmentation

Abstract

Multi-institutional collaboration is an emerging deployment of medical imaging processing with the goal to address the scarce annotation problem. While most of the efforts in this domain focus on the supervised machine learning models and the model performance improvement, there lack the discussion about the distributed system performance, such as the trade-off between collaboration and efficiency, i.e., communication cost and processing time. In this work, we propose a distributed system based on deep reinforcement learning for medical image segmentation. Preliminary experiments are conducted on single and multiple CPU and GPU environments to demonstrate the system performance and the trade-off. We highlight some insights for better designs of multi-institutional collaboration in the future.

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