arXiv cs.AI· Shaikh Akib Shahriyar, Zinedine Sehili, Adam Arthur, Christopher Schwartz·· 13 小时前AI 评分31
从数据增强到合成证据：在数据集入口评估生成的胸部X光片
From Augmentation to Synthetic Evidence: Evaluating Generated Chest Radiographs at Dataset Intake
AI 导读
研究将生成胸部X光片的审核拆为质量检查、来源分类与采集记录核验三个问题，实验用 Qwen 从 1000 张 Cheff-identified 或 SynthCheX 候选中最多选 20 张，MedGemma 评质量、基于冻结 DINOv3 特征的分类器判来源。
正文 · 原文
Abstract:A generated chest radiograph can be useful as declared augmentation, but the same image becomes unsupported evidence when submitted as the record of a real examination. We study this problem by separating three questions: whether an image passes an automated quality check, whether an origin classifier flags it as generated, and whether a trusted record supports the claimed acquisition. In our experiments, Qwen selects up to 20 images from pools of 1,000 existing Cheff-identified or SynthCheX candidates. A receiver evaluates quality with MedGemma and predicted origin with a classifier on frozen DINOv3 features. Quality-based selection raises quality passes from 12 to 19 of 20 for Cheff-identified images and from 8 to 17 for SynthCheX. We find no evidence that selection improves passage through both checks: no selected Cheff-identified image passes both, while uniform, quality, and quality-constrained surrogate selection produce 2, 1, and 2 SynthCheX passes. On unprocessed test images, the origin classifier flags 4,677 of 5,000 SynthCheX images, but it also misclassify 110 of 2,297 NIH (real) images and 568 of 957 genuine images from another collection as fake. Our results further show that JPEG compression reduces SynthCheX-versus-NIH AUROC from 0.987 to 0.841. Even without processing, 152 SynthCheX images pass both pixel checks. These checks can filter submissions, but they cannot verify an acquisition claim. We strongly advocate that source and intended use must be recorded and checked separately for admission of chest radiographs in medical datasets.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.29152 [cs.CV] |
| (or arXiv:2610.29152v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.29152 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shaikh Akib Shahriyar [view email]
[v1]
Wed, 21 Oct 2026 03:39:28 UTC (2,488 KB)
来源：arXiv cs.AI · arxiv.org