Black-box deep learning approaches have showcased significant potential in
the realm of medical image analysis. However, the stringent trustworthiness
requirements intrinsic to the medical field have catalyzed research into the
utilization of Explainable Artificial Intelligence (XAI), with a particular
focus on concept-based methods. Existing concept-based methods predominantly
apply concept annotations from a single perspective (e.g., global level),
neglecting the nuanced semantic relationships between sub-regions and concepts
embedded within medical images. This leads to underutilization of the valuable
medical information and may cause models to fall short in harmoniously
balancing interpretability and performance when employing inherently
interpretable architectures such as Concept Bottlenecks. To mitigate these
shortcomings, we propose a multi-modal explainable disease diagnosis framework
that meticulously aligns medical images and clinical-related concepts
semantically at multiple strata, encompassing the image level, token level, and
concept level. Moreover, our method allows for model intervention and offers
both 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 label
efficiency for concept detection and disease diagnosis.

通过使用黑盒深度学习方法，并结合可解释的人工智能和基于概念的方法，在医学图像分析领域提出一种多模态可解释疾病诊断框架，以实现模型可解释性、高性能和标签效率的平衡。

MICA：通过多级图像概念对齐实现可解释性皮肤病病变诊断

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

The completeness axiom renders the explanation of a post-hoc XAI method only
locally faithful to the model, i.e. for a single decision. For the trustworthy
application of XAI, in particular for high-stake decisions, a more global model
understanding is required. Recently, concept-based methods have been proposed,
which are however not guaranteed to be bound to the actual model reasoning. To
circumvent this problem, we propose Multi-dimensional Concept Discovery (MCD)
as an extension of previous approaches that fulfills a completeness relation on
the level of concepts. Our method starts from general linear subspaces as
concepts and does neither require reinforcing concept interpretability nor
re-training of model parts. We propose sparse subspace clustering to discover
improved concepts and fully leverage the potential of multi-dimensional
subspaces. MCD offers two complementary analysis tools for concepts in input
space: (1) concept activation maps, that show where a concept is expressed
within a sample, allowing for concept characterization through prototypical
samples, and (2) concept relevance heatmaps, that decompose the model decision
into concept contributions. Both tools together enable a detailed understanding
of the model reasoning, which is guaranteed to relate to the model via a
completeness relation. This paves the way towards more trustworthy
concept-based XAI. We empirically demonstrate the superiority of MCD against
more constrained concept definitions.

本文提出了多维概念发现（MCD）作为一种基于输入空间的概念表征方法，其通过提出稀疏子空间聚类来发现改进的概念，并完全利用多维子空间的潜力，该方法给出了两个互补的分析工具来解释模型推理，为概念基础的 XAI 提供了更加可靠的基础。