  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.
