Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution
<|reference_start|>Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution: 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 https://github.com/CUHK-AIM-Group/STAR-RL.<|reference_end|>
@article{chen2024spatial-temporal,
    title={Spatial-temporal Hierarchical Reinforcement Learning for Interpretable
  Pathology Image Super-Resolution},
    author={Wenting Chen, Jie Liu, Tommy W.S. Chow, Yixuan Yuan},
    journal={arXiv preprint arXiv:2406.18310},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18310},
    primaryClass={cs.CV cs.LG eess.IV}
}