Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis
Junlin Hou Jilan Xu Hao Chen
The black-box nature of deep learning models has raised concerns about their
interpretability for successful deployment in real-world clinical applications.
To address the concerns, eXplainable Artificial Intelligence (XAI) aims to
provide clear and understandable explanations of the decision-making process.
In the medical domain, concepts such as attributes of lesions or abnormalities
serve as key evidence for deriving diagnostic results. However, existing
concept-based models mainly depend on concepts that appear independently and
require fine-grained concept annotations such as bounding boxes. A medical
image usually contains multiple concepts and the fine-grained concept
annotations are difficult to acquire. In this paper, we propose a novel
Concept-Attention Whitening (CAW) framework for interpretable skin lesion
diagnosis. CAW is comprised of a disease diagnosis branch and a concept
alignment branch. In the former branch, we train the CNN with a CAW layer
inserted to perform skin lesion diagnosis. The CAW layer decorrelates features
and aligns image features to conceptual meanings via an orthogonal matrix. In
the latter branch, we calculate the orthogonal matrix under the guidance of the
concept attention mask. We particularly introduce a weakly-supervised concept
mask generator that only leverages coarse concept labels for filtering local
regions that are relevant to certain concepts, improving the optimization of
the orthogonal matrix. Extensive experiments on two public skin lesion
diagnosis datasets demonstrated that CAW not only enhanced interpretability but
also maintained a state-of-the-art diagnostic performance.