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 of disease and treatment interventions, limiting practical applicability. To address this, we train a text-to-image diffusion model on multiple chest X-ray datasets and introduce a new editing method RadEdit that uses multiple masks, if present, to constrain changes and ensure consistency in the edited images. We consider three types of dataset shifts: acquisition shift, manifestation shift, and population shift, and demonstrate that our approach can diagnose failures and quantify model robustness without additional data collection, complementing more qualitative tools for explainable AI.

翻译：生物医学影像数据集通常规模较小且存在偏差，这意味着预测模型在实际环境中的表现可能远低于内部测试的预期。本研究提出利用生成式图像编辑技术模拟数据集偏移，并诊断生物医学视觉模型的故障模式；该方法可在模型部署前评估其就绪程度，从而降低成本和患者风险。现有编辑方法可能产生不良变化，由于疾病与治疗干预共同出现而学到虚假相关性，限制了实际应用性。为解决这一问题，我们在多个胸部X射线数据集上训练文本到图像扩散模型，并引入一种新的编辑方法RadEdit，该方法在存在多个掩膜时使用它们约束变化并确保编辑图像的一致性。我们考虑了三种数据集偏移类型：采集偏移、表现偏移和人群偏移，并证明该方法无需额外数据收集即可诊断故障和量化模型鲁棒性，补充了可解释AI的定性分析工具。