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arXiv · 2504.05227

A Reality Check of Vision-Language Pre-training in Radiology: Have We Progressed Using Text?

Julio Silva-Rodríguez·Jose Dolz·Ismail Ben Ayed
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Abstract

Vision-language pre-training has recently gained popularity as it allows learning rich feature representations using large-scale data sources. This paradigm has quickly made its way into the medical image analysis community. In particular, there is an impressive amount of recent literature developing vision-language models for radiology. However, the available medical datasets with image-text supervision are scarce, and medical concepts are fine-grained, involving expert knowledge that existing vision-language models struggle to encode. In this paper, we propose to take a prudent step back from the literature and revisit supervised, unimodal pre-training, using fine-grained labels instead. We conduct an extensive comparison demonstrating that unimodal pre-training is highly competitive and better suited to integrating heterogeneous data sources. Our results also question the potential of recent vision-language models for open-vocabulary generalization, which have been evaluated using optimistic experimental settings. Finally, we study novel alternatives to better integrate fine-grained labels and noisy text supervision.

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Julio Silva-Rodríguez, Jose Dolz, Ismail Ben Ayed. 2025-04-07. A Reality Check of Vision-Language Pre-training in Radiology: Have We Progressed Using Text?. https://arxiv.org/abs/2504.05227

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