Diagnosis-oriented Medical Image Compression with Efficient Transfer
  Learning
Guangqi Xie Xin Li Xiaohan Pan Zhibo Chen
Remote medical diagnosis has emerged as a critical and indispensable
technique in practical medical systems, where medical data are required to be
efficiently compressed and transmitted for diagnosis by either professional
doctors or intelligent diagnosis devices. In this process, a large amount of
redundant content irrelevant to the diagnosis is subjected to high-fidelity
coding, leading to unnecessary transmission costs. To mitigate this, we propose
diagnosis-oriented medical image compression, a special semantic compression
task designed for medical scenarios, targeting to reduce the compression cost
without compromising the diagnosis accuracy. However, collecting sufficient
medical data to optimize such a compression system is significantly expensive
and challenging due to privacy issues and the lack of professional annotation.
In this study, we propose DMIC, the first efficient transfer learning-based
codec, for diagnosis-oriented medical image compression, which can be
effectively optimized with only few-shot annotated medical examples, by reusing
the knowledge in the existing reinforcement learning-based task-driven semantic
coding framework, i.e., HRLVSC [1]. Concretely, we focus on tuning only the
partial parameters of the policy network for bit allocation within HRLVSC,
which enables it to adapt to the medical images. In this work, we validate our
DMIC with the typical medical task, Coronary Artery Segmentation. Extensive
experiments have demonstrated that our DMIC can achieve 47.594%BD-Rate savings
compared to the HEVC anchor, by tuning only the A2C module (2.7% parameters) of
the policy network with only 1 medical sample.