Summary of Concept Complement Bottleneck Model For Interpretable Medical Image Diagnosis, by Hongmei Wang et al.
Concept Complement Bottleneck Model for Interpretable Medical Image Diagnosis
by Hongmei Wang, Junlin Hou, Hao Chen
First submitted to arxiv on: 20 Oct 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 This paper proposes a novel approach to interpretable medical image diagnosis by complementing existing concept sets with new, discoverable concepts. The authors introduce a “concept complement bottleneck” model that leverages concept adapters to mine differences between known and unknown concepts, while also jointly utilizing known concepts to improve performance. The proposed strategy learns new concepts while maintaining model interpretability through diverse explanations. Experimental results on medical datasets demonstrate the outperformance of the proposed method in concept detection and disease diagnosis tasks compared to state-of-the-art competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about making artificial intelligence more trustworthy by letting it understand what it’s doing. Right now, AI models can explain their decisions, but they rely on having a lot of information beforehand. This makes them not very good at dealing with incomplete or low-quality information. The authors propose a new way to teach AI to find new concepts that aren’t defined yet and to use those concepts to make better diagnoses. They test this approach on medical images and show that it’s more accurate than other methods. |
