Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis
<|reference_start|>Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis: 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.<|reference_end|>
@article{gao2024evidential,
    title={Evidential Concept Embedding Models: Towards Reliable Concept
  Explanations for Skin Disease Diagnosis},
    author={Yibo Gao and Zheyao Gao and Xin Gao and Yuanye Liu and Bomin Wang and
  Xiahai Zhuang},
    journal={arXiv preprint arXiv:2406.19130},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19130},
    primaryClass={cs.CV}
}