XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via
  Concept-guided Context Optimization
Yequan Bie Luyang Luo Zhixuan Chen Hao Chen
Utilizing potent representations of the large vision-language models (VLMs)
to accomplish various downstream tasks has attracted increasing attention.
Within this research field, soft prompt learning has become a representative
approach for efficiently adapting VLMs such as CLIP, to tasks like image
classification. However, most existing prompt learning methods learn text
tokens that are unexplainable, which cannot satisfy the stringent
interpretability requirements of Explainable Artificial Intelligence (XAI) in
high-stakes scenarios like healthcare. To address this issue, we propose a
novel explainable prompt learning framework that leverages medical knowledge by
aligning the semantics of images, learnable prompts, and clinical
concept-driven prompts at multiple granularities. Moreover, our 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 and explainability analyses conducted on various
datasets, with and without concept labels, demonstrate that our method
simultaneously achieves superior diagnostic performance, flexibility, and
interpretability, shedding light on the effectiveness of foundation models in
facilitating XAI. The code will be made publically available.