Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?

• URL: http://arxiv.org/abs/2410.13523v1
• Date: Thu, 17 Oct 2024 13:11:07 GMT
• Title: Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?
• Authors: Che Liu, Zhongwei Wan, Haozhe Wang, Yinda Chen, Talha Qaiser, Chen Jin, Fariba Yousefi, Nikolay Burlutskiy, Rossella Arcucci,
• Abstract summary: Training medical vision-language pre-training models requires datasets with paired, high-quality image-text data. Recent advancements in Large Language Models have made it possible to generate large-scale synthetic image-text pairs. We propose an automated pipeline to build a diverse, high-quality synthetic dataset.
• Score: 8.775988650381397
• License:
• Abstract: Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling zero-shot tasks for medical image understanding. However, training MedVLP models typically requires large-scale datasets with paired, high-quality image-text data, which are scarce in the medical domain. Recent advancements in Large Language Models (LLMs) and diffusion models have made it possible to generate large-scale synthetic image-text pairs. This raises the question: *Can MedVLP succeed using purely synthetic data?* To address this, we use off-the-shelf generative models to create synthetic radiology reports and paired Chest X-ray (CXR) images, and propose an automated pipeline to build a diverse, high-quality synthetic dataset, enabling a rigorous study that isolates model and training settings, focusing entirely from the data perspective. Our results show that MedVLP models trained *exclusively on synthetic data* outperform those trained on real data by **3.8%** in averaged AUC on zero-shot classification. Moreover, using a combination of synthetic and real data leads to a further improvement of **9.07%**. Additionally, MedVLP models trained on synthetic or mixed data consistently outperform those trained on real data in zero-shot grounding, as well as in fine-tuned classification and segmentation tasks. Our analysis suggests MedVLP trained on well-designed synthetic data can outperform models trained on real datasets, which may be limited by low-quality samples and long-tailed distributions.

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