Deep Reinforcement Learning Framework for Thoracic Diseases
  Classification via Prior Knowledge Guidance
Weizhi Nie Chen Zhang Dan Song Lina Zhao Yunpeng Bai Keliang Xie Anan Liu
The chest X-ray is often utilized for diagnosing common thoracic diseases. In
recent years, many approaches have been proposed to handle the problem of
automatic diagnosis based on chest X-rays. However, the scarcity of labeled
data for related diseases still poses a huge challenge to an accurate
diagnosis. In this paper, we focus on the thorax disease diagnostic problem and
propose a novel deep reinforcement learning framework, which introduces prior
knowledge to direct the learning of diagnostic agents and the model parameters
can also be continuously updated as the data increases, like a person's
learning process. Especially, 1) prior knowledge can be learned from the
pre-trained model based on old data or other domains' similar data, which can
effectively reduce the dependence on target domain data, and 2) the framework
of reinforcement learning can make the diagnostic agent as exploratory as a
human being and improve the accuracy of diagnosis through continuous
exploration. The method can also effectively solve the model learning problem
in the case of few-shot data and improve the generalization ability of the
model. Finally, our approach's performance was demonstrated using the
well-known NIH ChestX-ray 14 and CheXpert datasets, and we achieved competitive
results. The source code can be found here:
\url{https://github.com/NeaseZ/MARL}.