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}.

翻译：胸部X光片常被用于诊断常见胸部疾病。近年来，许多方法被提出以处理基于胸部X光片的自动诊断问题。然而，相关疾病的标注数据稀缺仍对准确诊断构成巨大挑战。本文聚焦于胸部疾病诊断问题，提出了一种新颖的深度强化学习框架，该框架引入先验知识来指导诊断智能体的学习过程，且模型参数可随数据增长持续更新，类似人类的学习过程。具体而言：1）基于旧数据或其他领域相似数据的预训练模型可学习先验知识，有效降低对目标领域数据的依赖；2）强化学习框架能使诊断智能体具备类似人类的探索性，并通过持续探索提升诊断准确率。该方法还能有效解决小样本数据下的模型学习问题，提升模型泛化能力。最后，我们在著名的NIH ChestX-ray 14和CheXpert数据集上验证了方法性能，取得了具有竞争力的结果。源代码见：\url{https://github.com/NeaseZ/MARL}。