RadEdit: stress-testing biomedical vision models via diffusion image editing

arXiv:2312.12865v3 Announce Type: replace
Abstract: Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. This work proposes using generative image editing to simulate dataset shifts and diagnose failure modes of biomedical vision models; this can be used in advance of deployment to assess readiness, potentially reducing cost and patient harm. Existing editing methods can produce undesirable changes, with spurious correlations learned due to the co-occurrence …

abstract arxiv biomedical cs.ai cs.cv dataset datasets diffusion editing failure generative image imaging meaning performance predictive predictive models small stress testing type via vision vision models work world