Pathology image are essential for accurately interpreting lesion cells in
cytopathology screening, but acquiring high-resolution digital slides requires
specialized equipment and long scanning times. Though super-resolution (SR)
techniques can alleviate this problem, existing deep learning models recover
pathology image in a black-box manner, which can lead to untruthful biological
details and misdiagnosis. Additionally, current methods allocate the same
computational resources to recover each pixel of pathology image, leading to
the sub-optimal recovery issue due to the large variation of pathology image.
In this paper, we propose the first hierarchical reinforcement learning
framework named Spatial-Temporal hierARchical Reinforcement Learning (STAR-RL),
mainly for addressing the aforementioned issues in pathology image
super-resolution problem. We reformulate the SR problem as a Markov decision
process of interpretable operations and adopt the hierarchical recovery
mechanism in patch level, to avoid sub-optimal recovery. Specifically, the
higher-level spatial manager is proposed to pick out the most corrupted patch
for the lower-level patch worker. Moreover, the higher-level temporal manager
is advanced to evaluate the selected patch and determine whether the
optimization should be stopped earlier, thereby avoiding the over-processed
problem. Under the guidance of spatial-temporal managers, the lower-level patch
worker processes the selected patch with pixel-wise interpretable actions at
each time step. Experimental results on medical images degraded by different
kernels show the effectiveness of STAR-RL. Furthermore, STAR-RL validates the
promotion in tumor diagnosis with a large margin and shows generalizability
under various degradations. The source code is available at
this https URL

提出了一种层次强化学习框架，用于解决病理图像超分辨率问题中的问题，通过分级恢复机制和时空管理器实现对病理图像的逐块恢复，并在肿瘤诊断方面取得显著提升。