Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis
This work addresses interpretability concerns for deploying AI in clinical settings, though it is incremental by building on existing concept-based methods.
The paper tackled the problem of interpreting deep learning models for skin lesion diagnosis by aligning latent space axes with known medical concepts, achieving state-of-the-art diagnostic performance on two public datasets.
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. Existing concept-based models mainly depend on concepts that appear independently and require fine-grained concept annotations such as bounding boxes. However, a medical image usually contains multiple concepts, and the fine-grained concept annotations are difficult to acquire. In this paper, we aim to interpret representations in deep neural networks by aligning the axes of the latent space with known concepts of interest. 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 a convolutional neural network (CNN) with an inserted CAW layer 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, the orthogonal matrix is calculated 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.