bibtext: @InProceedings{ Gao_Evidential_MICCAI2024,
                   author = { Gao, Yibo and Gao, Zheyao and Gao, Xin and Liu, Yuanye and Wang, Bomin and Zhuang, Xiahai },
                   title = { { Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
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.
title: Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis
authors: Gao, Yibo Gao, Zheyao Gao, Xin Liu, Yuanye Wang, Bomin Zhuang, Xiahai