Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning

Chong Ma; Hanqi Jiang; Wenting Chen; Yiwei Li; Zihao Wu; Xiaowei Yu; Zhengliang Liu; Lei Guo; Dajiang Zhu; Tuo Zhang; Dinggang Shen; Tianming Liu; Xiang Li

arXiv · 2403.12416

Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning

Chong Ma·Hanqi Jiang·Wenting Chen·Yiwei Li·Zihao Wu·Xiaowei Yu·Zhengliang Liu·Lei Guo·Dajiang Zhu·Tuo Zhang·Dinggang Shen·Tianming Liu·Xiang Li

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Abstract

In the medical multi-modal frameworks, the alignment of cross-modality features presents a significant challenge. However, existing works have learned features that are implicitly aligned from the data, without considering the explicit relationships in the medical context. This data-reliance may lead to low generalization of the learned alignment relationships. In this work, we propose the Eye-gaze Guided Multi-modal Alignment (EGMA) framework to harness eye-gaze data for better alignment of medical visual and textual features. We explore the natural auxiliary role of radiologists' eye-gaze data in aligning medical images and text, and introduce a novel approach by using eye-gaze data, collected synchronously by radiologists during diagnostic evaluations. We conduct downstream tasks of image classification and image-text retrieval on four medical datasets, where EGMA achieved state-of-the-art performance and stronger generalization across different datasets. Additionally, we explore the impact of varying amounts of eye-gaze data on model performance, highlighting the feasibility and utility of integrating this auxiliary data into multi-modal alignment framework.

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Chong Ma, Hanqi Jiang, Wenting Chen, Yiwei Li, Zihao Wu, Xiaowei Yu, Zhengliang Liu, Lei Guo, Dajiang Zhu, Tuo Zhang, Dinggang Shen, Tianming Liu, Xiang Li. 2024-06-14. Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning. https://arxiv.org/abs/2403.12416

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