Integrating Clinical Knowledge into Concept Bottleneck Models
<|reference_start|>Integrating Clinical Knowledge into Concept Bottleneck Models: Concept bottleneck models (CBMs), which predict human-interpretable concepts (e.g., nucleus shapes in cell images) before predicting the final output (e.g., cell type), provide insights into the decision-making processes of the model. However, training CBMs solely in a data-driven manner can introduce undesirable biases, which may compromise prediction performance, especially when the trained models are evaluated on out-of-domain images (e.g., those acquired using different devices). To mitigate this challenge, we propose integrating clinical knowledge to refine CBMs, better aligning them with clinicians' decision-making processes. Specifically, we guide the model to prioritize the concepts that clinicians also prioritize. We validate our approach on two datasets of medical images: white blood cell and skin images. Empirical validation demonstrates that incorporating medical guidance enhances the model's classification performance on unseen datasets with varying preparation methods, thereby increasing its real-world applicability.<|reference_end|>
@article{pang2024integrating,
    title={Integrating Clinical Knowledge into Concept Bottleneck Models},
    author={Winnie Pang, Xueyi Ke, Satoshi Tsutsui, and Bihan Wen},
    journal={arXiv preprint arXiv:2407.06600},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06600},
    primaryClass={cs.CV}
}