Summary of Can Medical Vision-language Pre-training Succeed with Purely Synthetic Data?, by Che Liu et al.
Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?
by Che Liu, Zhongwei Wan, Haozhe Wang, Yinda Chen, Talha Qaiser, Chen Jin, Fariba Yousefi, Nikolay Burlutskiy, Rossella Arcucci
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary 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. This paper investigates whether MedVLP can succeed using purely synthetic data. The authors 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. Their 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores whether computers can be trained to understand medical images without using actual patient data. Traditionally, this kind of training requires a lot of paired image-text data, which is hard to come by in the medical field. The scientists behind this project used computer algorithms to generate fake radiology reports and matching chest X-ray images. They then created an automated system to build a large dataset of these synthetic images. Surprisingly, their results show that computers trained on these fake images can actually perform better than those trained on real data! This has important implications for how we train computers to analyze medical images in the future. |
Keywords
» Artificial intelligence » Auc » Classification » Grounding » Synthetic data » Zero shot
