Bibliographic Details
| Title: |
Delving into Out-of-Distribution Detection with Medical Vision-Language Models |
| Authors: |
Ju, Lie, Zhou, Sijin, Zhou, Yukun, Lu, Huimin, Zhu, Zhuoting, Keane, Pearse A., Ge, Zongyuan |
| Publication Year: |
2025 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computer Vision and Pattern Recognition |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2503.01020 |
| Accession Number: |
edsarx.2503.01020 |
| Database: |
arXiv |
