Integrating Clinical Knowledge into Concept Bottleneck Models

• URL: http://arxiv.org/abs/2407.06600v1
• Date: Tue, 9 Jul 2024 07:03:42 GMT
• Title: Integrating Clinical Knowledge into Concept Bottleneck Models
• Authors: Winnie Pang, Xueyi Ke, Satoshi Tsutsui, Bihan Wen,
• Abstract summary: Concept bottleneck models (CBMs) predict human-interpretable concepts before predicting the final output. We propose integrating clinical knowledge to refine CBMs, better aligning them with clinicians' decision-making processes. We validate our approach on two datasets of medical images: white blood cell and skin images.
• Score: 18.26357481872999
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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