From Augmentation to Synthetic Evidence: Evaluating Generated Chest Radiographs at Dataset Intake

Key points

• 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.1
• 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.1
• 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.1
• Our results further show that JPEG compression reduces SynthCheX-versus-NIH AUROC from 0.987 to 0.841.1

Sources (1)

• [1]From Augmentation to Synthetic Evidence: Evaluating Generated Chest Radiographs at Dataset Intake
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 03:39 AM

  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.

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