Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis
- URL: http://arxiv.org/abs/2406.19130v1
- Date: Thu, 27 Jun 2024 12:29:50 GMT
- Title: Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis
- Authors: Yibo Gao, Zheyao Gao, Xin Gao, Yuanye Liu, Bomin Wang, Xiahai Zhuang,
- Abstract summary: Concept Bottleneck Models (CBM) have emerged as an active interpretable framework incorporating human-interpretable concepts into decision-making.
We propose an evidential Concept Embedding Model (evi-CEM) which employs evidential learning to model the concept uncertainty.
Our evaluation demonstrates that evi-CEM achieves superior performance in terms of concept prediction.
- Score: 24.946148305384202
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Due to the high stakes in medical decision-making, there is a compelling demand for interpretable deep learning methods in medical image analysis. Concept Bottleneck Models (CBM) have emerged as an active interpretable framework incorporating human-interpretable concepts into decision-making. However, their concept predictions may lack reliability when applied to clinical diagnosis, impeding concept explanations' quality. To address this, we propose an evidential Concept Embedding Model (evi-CEM), which employs evidential learning to model the concept uncertainty. Additionally, we offer to leverage the concept uncertainty to rectify concept misalignments that arise when training CBMs using vision-language models without complete concept supervision. With the proposed methods, we can enhance concept explanations' reliability for both supervised and label-efficient settings. Furthermore, we introduce concept uncertainty for effective test-time intervention. Our evaluation demonstrates that evi-CEM achieves superior performance in terms of concept prediction, and the proposed concept rectification effectively mitigates concept misalignments for label-efficient training. Our code is available at https://github.com/obiyoag/evi-CEM.
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