The deep neural network is a research hotspot for histopathological image analysis, which can improve the efficiency and accuracy of diagnosis for pathologists or be used for disease screening. The whole slide pathological image can reach one gigapixel and contains abundant tissue feature information, which needs to be divided into a lot of patches in the training and inference stages. This will lead to a long convergence time and large memory consumption. Furthermore, well-annotated data sets are also in short supply in the field of digital pathology. Inspired by the pathologist's clinical diagnosis process, we propose a weakly supervised deep reinforcement learning framework, which can greatly reduce the time required for network inference. We use neural network to construct the search model and decision model of reinforcement learning agent respectively. The search model predicts the next action through the image features of different magnifications in the current field of view, and the decision model is used to return the predicted probability of the current field of view image. In addition, an expert-guided model is constructed by multi-instance learning, which not only provides rewards for search model, but also guides decision model learning by the knowledge distillation method. Experimental results show that our proposed method can achieve fast inference and accurate prediction of whole slide images without any pixel-level annotations.

翻译：深心神经网络是一个研究热点,用于组织病理学图像分析,可以提高病理学家诊断的效率和准确性,或用于疾病筛查。整个幻灯片病理学图像可以达到一个千兆像素,含有丰富的组织特征信息,需要在培训和推断阶段将其分为许多补丁,从而导致长期趋同和大量记忆消耗。此外,在数字病理学领域,附带说明的数据集也处于短缺状态。在病理学家临床诊断过程的启发下,我们建议建立一个监督不力的深度强化学习框架,这可以大大缩短网络推断所需的时间。我们使用神经网络来构建强化学习剂的搜索模型和决定模型。搜索模型预测下一个行动,通过当前视觉领域不同放大图像的图像特征进行预测,并使用决定模型来恢复当前视觉图像领域的预测概率。此外,专家制导模型是多因学习而构建的,不仅为搜索模型提供奖励,而且还可以指导任何快速图像预测,同时指导我们通过快速分析方法进行决定模型学习。