bibtext: @InProceedings{ Pan_Integrating_MICCAI2024,
                   author = { Pang, Winnie and Ke, Xueyi and Tsutsui, Satoshi and Wen, Bihan },
                   title = { { Integrating Clinical Knowledge into Concept Bottleneck Models } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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.
title: Integrating Clinical Knowledge into Concept Bottleneck Models
authors: Pang, Winnie Ke, Xueyi Tsutsui, Satoshi Wen, Bihan