Summary of Spatial-temporal Hierarchical Reinforcement Learning For Interpretable Pathology Image Super-resolution, by Wenting Chen et al.
Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution
by Wenting Chen, Jie Liu, Tommy W.S. Chow, Yixuan Yuan
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A hierarchical reinforcement learning framework, called Spatial-Temporal hierARchical Reinforcement Learning (STAR-RL), is proposed to address the issues of untruthful biological details and misdiagnosis in pathology image super-resolution. The existing deep learning models recover pathology images in a black-box manner, which can lead to inaccurate interpretations. STAR-RL reformulates the SR problem as a Markov decision process and adopts a hierarchical recovery mechanism at the patch level. This approach avoids sub-optimal recovery by selecting the most corrupted patch for processing. The framework also includes higher-level spatial and temporal managers that evaluate the selected patch and determine whether optimization should be stopped earlier, avoiding over-processing. Experimental results on medical images degraded by different kernels demonstrate the effectiveness of STAR-RL in promoting tumor diagnosis with a large margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary STAR-RL is a new way to improve pathology image super-resolution. Usually, it takes special equipment and a long time to get good quality digital slides. But sometimes, this process can be unreliable and even lead to mistakes. The new method uses something called reinforcement learning to find the best way to fix these images. It works by breaking down the image into small parts and then fixing each part one at a time. This helps avoid making things worse by trying to fix too much at once. The results show that this method can help doctors make more accurate diagnoses. |
Keywords
* Artificial intelligence * Deep learning * Optimization * Reinforcement learning * Super resolution
