Summary of Mica: Towards Explainable Skin Lesion Diagnosis Via Multi-level Image-concept Alignment, by Yequan Bie et al.
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment
by Yequan Bie, Luyang Luo, Hao Chen
First submitted to arxiv on: 16 Jan 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 paper proposes a multi-modal explainable framework for disease diagnosis using medical images, which addresses the limitations of existing concept-based methods. The approach aligns medical images with clinical-related concepts at multiple levels, including image, token, and concept levels, allowing for model intervention and human-interpretable explanations. Experimental results on three skin image datasets demonstrate high performance and label efficiency for concept detection and disease diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about using artificial intelligence to help doctors diagnose diseases from medical images. Right now, AI models can’t explain why they’re making certain diagnoses, which isn’t good enough for the medical field. The researchers came up with a new way to make these AI models more trustworthy by matching medical images with medical concepts at different levels. This allows the model to give explanations that humans can understand and also improves its ability to detect diseases. |
Keywords
» Artificial intelligence » Multi modal » Token
