Fairness Analysis of CLIP-Based Foundation Models for X-Ray Image Classification

• URL: http://arxiv.org/abs/2501.19086v1
• Date: Fri, 31 Jan 2025 12:23:50 GMT
• Title: Fairness Analysis of CLIP-Based Foundation Models for X-Ray Image Classification
• Authors: Xiangyu Sun, Xiaoguang Zou, Yuanquan Wu, Guotai Wang, Shaoting Zhang,
• Abstract summary: We perform a comprehensive fairness analysis of CLIP-like models applied to X-ray image classification. We assess their performance and fairness across diverse patient demographics and disease categories using zero-shot inference and various fine-tuning techniques.
• Score: 15.98427699337596
• License:
• Abstract: X-ray imaging is pivotal in medical diagnostics, offering non-invasive insights into a range of health conditions. Recently, vision-language models, such as the Contrastive Language-Image Pretraining (CLIP) model, have demonstrated potential in improving diagnostic accuracy by leveraging large-scale image-text datasets. However, since CLIP was not initially designed for medical images, several CLIP-like models trained specifically on medical images have been developed. Despite their enhanced performance, issues of fairness - particularly regarding demographic attributes - remain largely unaddressed. In this study, we perform a comprehensive fairness analysis of CLIP-like models applied to X-ray image classification. We assess their performance and fairness across diverse patient demographics and disease categories using zero-shot inference and various fine-tuning techniques, including Linear Probing, Multilayer Perceptron (MLP), Low-Rank Adaptation (LoRA), and full fine-tuning. Our results indicate that while fine-tuning improves model accuracy, fairness concerns persist, highlighting the need for further fairness interventions in these foundational models.

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