Delving into Out-of-Distribution Detection with Medical Vision-Language Models

• URL: http://arxiv.org/abs/2503.01020v1
• Date: Sun, 02 Mar 2025 21:09:51 GMT
• Title: Delving into Out-of-Distribution Detection with Medical Vision-Language Models
• Authors: Lie Ju, Sijin Zhou, Yukun Zhou, Huimin Lu, Zhuoting Zhu, Pearse A. Keane, Zongyuan Ge,
• Abstract summary: We conduct the first systematic investigation into the OOD detection potential of medical vision-language models.<n>To accurately reflect real-world challenges, we introduce a cross-modality evaluation benchmarking pipeline for full-spectrum OOD detection.<n>We propose a novel hierarchical prompt-based method that significantly enhances OOD detection performance.
• Score: 14.286027727962104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in medical vision-language models (VLMs) demonstrate impressive performance in image classification tasks, driven by their strong zero-shot generalization capabilities. However, given the high variability and complexity inherent in medical imaging data, the ability of these models to detect out-of-distribution (OOD) data in this domain remains underexplored. In this work, we conduct the first systematic investigation into the OOD detection potential of medical VLMs. We evaluate state-of-the-art VLM-based OOD detection methods across a diverse set of medical VLMs, including both general and domain-specific purposes. To accurately reflect real-world challenges, we introduce a cross-modality evaluation pipeline for benchmarking full-spectrum OOD detection, rigorously assessing model robustness against both semantic shifts and covariate shifts. Furthermore, we propose a novel hierarchical prompt-based method that significantly enhances OOD detection performance. Extensive experiments are conducted to validate the effectiveness of our approach. The codes are available at https://github.com/PyJulie/Medical-VLMs-OOD-Detection.

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