Summary of Xcoop: Explainable Prompt Learning For Computer-aided Diagnosis Via Concept-guided Context Optimization, by Yequan Bie et al.
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization
by Yequan Bie, Luyang Luo, Zhixuan Chen, Hao Chen
First submitted to arxiv on: 14 Mar 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 explainable prompt learning framework for adapting large vision-language models (VLMs) like CLIP to tasks such as image classification. Unlike existing methods, which learn unexplainable text tokens, this approach leverages medical knowledge by aligning the semantics of images, learnable prompts, and clinical concept-driven prompts at multiple granularities. The proposed framework addresses the lack of valuable concept annotations by eliciting knowledge from large language models and offers both visual and textual explanations for the prompts. Extensive experiments on various datasets demonstrate that this method achieves superior diagnostic performance, flexibility, and interpretability, shedding light on the effectiveness of foundation models in facilitating Explainable Artificial Intelligence (XAI). The proposed framework is designed to satisfy the stringent interpretability requirements of XAI in high-stakes scenarios like healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to use big language models for image classification. Most current methods learn text prompts that are hard to understand, but this approach uses medical knowledge to make those prompts more transparent and easier to explain. The method is tested on different datasets and shows it can achieve good results while being easy to understand. This could be useful in fields like healthcare where explanations are important. |
Keywords
» Artificial intelligence » Image classification » Prompt » Semantics
