Summary of A Large-scale Medical Visual Task Adaptation Benchmark, by Shentong Mo et al.
A Large-scale Medical Visual Task Adaptation Benchmark
by Shentong Mo, Xufang Luo, Yansen Wang, Dongsheng Li
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Medium Difficulty summary: This research paper presents Med-VTAB, a large-scale benchmark for adapting pre-trained Vision Transformers (ViTs) to medical imaging tasks. The authors explore the effect of visual task adaptation on medical images from diverse modalities, such as color images, X-ray, and CT scans. They also study the scaling law of prompt tuning for medical tasks and the generalizability of visual adaptation using different pre-training weights. Furthermore, they introduce GMoE-Adapter, a novel method that combines medical and general pre-training weights to achieve state-of-the-art results in medical visual task adaptation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper helps computers learn better from medical images by adapting them for specific tasks. They created a big database of 1.68 million medical images to test different ways to adapt the computer’s vision. The authors found that using both general and medical image training data is best, and they introduced a new method called GMoE-Adapter that gets the best results. |
Keywords
» Artificial intelligence » Prompt
