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Black-box deep learning approaches have showcased significant potential inthe realm of medical image analysis. However, the stringent trustworthinessrequirements intrinsic to the medical field have catalyzed research into theutilization of Explainable Artificial Intelligence (XAI), with a particularfocus on concept-based methods. Existing concept-based methods predominantlyapply concept annotations from a single perspective (e.g., global level),neglecting the nuanced semantic relationships between sub-regions and conceptsembedded within medical images. This leads to underutilization of the valuablemedical information and may cause models to fall short in harmoniouslybalancing interpretability and performance when employing inherentlyinterpretable architectures such as Concept Bottlenecks. To mitigate theseshortcomings, we propose a multi-modal explainable disease diagnosis frameworkthat meticulously aligns medical images and clinical-related conceptssemantically at multiple strata, encompassing the image level, token level, andconcept level. Moreover, our method allows for model intervention and offersboth textual and visual explanations in terms of human-interpretable concepts.Experimental results on three skin image datasets demonstrate that our method,while preserving model interpretability, attains high performance and labelefficiency for concept detection and disease diagnosis.

MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment