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.

通过引入 Concept-Attention Whitening（CAW）框架，我们提出了一种用于解释性皮肤病变诊断的新方法。CAW 不仅提高了可解释性，还保持了最先进的诊断性能。

概念关注白化对于可解释性皮肤病诊断的应用

Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis

Cognitive diagnosis aims to diagnose students' knowledge proficiencies based
on their response scores on exam questions, which is the basis of many domains
such as computerized adaptive testing. Existing cognitive diagnosis models
(CDMs) follow a proficiency-response paradigm, which views diagnostic results
as learnable embeddings that are the cause of students' responses and learns
the diagnostic results through optimization. However, such a paradigm can
easily lead to unidentifiable diagnostic results and the explainability
overfitting problem, which is harmful to the quantification of students'
learning performance. To address these problems, we propose a novel
identifiable cognitive diagnosis framework. Specifically, we first propose a
flexible diagnostic module which directly diagnose identifiable and explainable
examinee traits and question features from response logs. Next, we leverage a
general predictive module to reconstruct response logs from the diagnostic
results to ensure the preciseness of the latter. We furthermore propose an
implementation of the framework, i.e., ID-CDM, to demonstrate the availability
of the former. Finally, we demonstrate the identifiability, explainability and
preciseness of diagnostic results of ID-CDM through experiments on four public
real-world datasets.

提出了一种新颖的可识别认知诊断框架，通过灵活的诊断模块直接诊断可识别和可解释的考生特征和问题特征，并利用一个通用的预测模块从诊断结果中重建响应日志，以确保后者的精确性。通过对四个公开真实数据集的实验，证明了 ID-CDM 的可识别性、可解释性和诊断结果的精确性。