Summary of Towards Multi-dimensional Explanation Alignment For Medical Classification, by Lijie Hu et al.
Towards Multi-dimensional Explanation Alignment for Medical Classification
by Lijie Hu, Songning Lai, Wenshuo Chen, Hongru Xiao, Hongbin Lin, Lu Yu, Jingfeng Zhang, Di Wang
First submitted to arxiv on: 28 Oct 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 The novel framework Med-MICN, Medical Multi-dimensional Interpretable Concept Network, addresses limitations in medical image analysis’s interpretable methods. It provides alignment across neural symbolic reasoning, concept semantics, and saliency maps, outperforming current approaches. Med-MICN boasts high prediction accuracy, interpretability across multiple dimensions, and automation through end-to-end concept labeling, reducing human training effort for new datasets. It surpasses baselines on four benchmark datasets, demonstrating its effectiveness and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medicine is trying to use computer images to help doctors understand patient conditions. But it’s hard because the computers don’t explain why they think something is wrong or what that means. This paper makes a special tool called Med-MICN that helps make these computers better. It does this by using three different ways to understand what the computer is seeing: symbols, words, and pictures. The new tool is very good at understanding medical images and can even teach itself how to work with new pictures without needing people to teach it. This makes doctors’ jobs easier and could help make medicine better. |
Keywords
» Artificial intelligence » Alignment » Semantics
