Summary of Agent Aggregator with Mask Denoise Mechanism For Histopathology Whole Slide Image Analysis, by Xitong Ling et al.
Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis
by Xitong Ling, Minxi Ouyang, Yizhi Wang, Xinrui Chen, Renao Yan, Hongbo Chu, Junru Cheng, Tian Guan, Sufang Tian, Xiaoping Liu, Yonghong He
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed AMD-MIL model is a novel approach to whole slide image (WSI) classification and region-of-interests (ROIs) localization in histopathology analysis. The model leverages agent aggregator with mask denoise mechanism to overcome the challenges of attention mechanisms, which fail to capture inter-instance information and cause quadratic computational complexity. AMD-MIL uses an agent token as an intermediate variable between query and key for computing instance importance, dynamically masking low-contribution representations and eliminating noise. This results in better attention allocation, capturing micro-metastases in cancer, and improving interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AMD-MIL is a new way to help doctors diagnose diseases by looking at pictures of tissues under a microscope. These pictures are very high-resolution and have lots of information that’s hard for computers to understand. AMD-MIL uses special tricks to make it easier for computers to analyze these pictures, which can help doctors find tiny signs of disease that they might miss otherwise. This makes it better than other methods that scientists have tried before. |
Keywords
» Artificial intelligence » Attention » Classification » Mask » Token
