Summary of Graph-guided Test-time Adaptation For Glaucoma Diagnosis Using Fundus Photography, by Qian Zeng et al.
Graph-Guided Test-Time Adaptation for Glaucoma Diagnosis using Fundus Photography
by Qian Zeng, Le Zhang, Yipeng Liu, Ce Zhu, Fan Zhang
First submitted to arxiv on: 5 Jul 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 Graph-guided Test-Time Adaptation (GTTA) framework is designed to improve the generalizability of deep learning models for diagnosing glaucoma from fundus images. The approach addresses domain shifts by incorporating topological information into model training, reducing the risk of spurious correlations. During inference, GTTA introduces a novel test-time training objective that adapts the source-trained classifier to target patterns with reliable class conditional estimation and consistency regularization. Experimental results demonstrate the superiority of the overall framework and its individual components under different backbone networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GTTA is a new way to make deep learning models better at diagnosing glaucoma from images taken by different cameras or in different places. The problem is that current models don’t work as well when they’re tested with images they haven’t seen before. GTTA fixes this by adding information about the shape and structure of the images into the model, so it can adapt to new situations. This helps reduce mistakes caused by spurious correlations. In tests, GTTA did better than other methods at diagnosing glaucoma from images taken in different places. |
Keywords
» Artificial intelligence » Deep learning » Inference » Regularization
