The NeurIPS 2024 Preshow: A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis

Medical image-analysis models often struggle with domain shifts, where differences between training and deployment data can reduce diagnostic accuracy because general-purpose architectures may rely on demographic confounders or spurious correlations rather than medically meaningful signals. The featured NeurIPS 2024 research by Yue Yang introduces Knowledge Enhanced Bottlenecks (KnoBo), a framework that embeds structured medical knowledge from sources such as textbooks and PubMed articles into model architectures through concept bottlenecks. Inspired by how clinicians learn foundational concepts before applying them to cases, KnoBo aims to improve robustness across varied medical imaging datasets while making model decisions more interpretable for clinicians. The approach may also be extended through multimodal inputs such as wearable-device and genomic data, and its transparent reasoning could support personalized, interactive tools for medical education.

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