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.

提出了一种基于弱监督深度强化学习框架的深度神经网络方法，可以快速有效地进行数字病理学领域中含有丰富组织特征信息的高分辨率全幅病理图像分析，而不需要基于像素级别的像素注释数据集。