Counterfactual reasoning is often used in a clinical setting to explain decisions or weigh alternatives. Therefore, for imaging based modalities such as ophthalmology, it would be beneficial to be able to create counterfactual images, illustrating the answer to the question: "If the subject had had diabetic retinopathy, how would the fundus image have looked?" Here, we demonstrate that using a diffusion model in combination with an adversarially robust classifier trained on retinal disease classification tasks enables generation of highly realistic counterfactuals of retinal fundus images and optical coherence tomorgraphy (OCT) B-scans. Ideally, these classifiers encode the salient features indicative for each disease class and can steer the diffusion model to show realistic disease signs or remove disease-related lesions in a realistic way. Importantly, in a user study, domain experts found the counterfactuals generated using our method significantly more realistic than counterfactuals generated from a previous method, and even indistiguishable from realistic images.

翻译：反事实推理在临床环境中常被用于解释决策或权衡替代方案。因此，对于眼科等基于影像的模态，能够生成反事实图像将颇具价值，用于阐释如下问题：“若受检者患有糖尿病视网膜病变，其眼底图像会呈现何种变化？”本文证明，将扩散模型与针对视网膜疾病分类任务训练的对抗鲁棒分类器相结合，可生成视网膜眼底图像和光学相干断层扫描B扫描图像的高度现实反事实。理想情况下，这些分类器能编码指示每种疾病类别的关键特征，并引导扩散模型以逼真的方式呈现疾病体征，或逼真地移除与病变相关的特征。重要的是，在用户研究中，领域专家认为采用我们的方法生成的反事实结果，其逼真度显著优于先前方法生成的反事实，甚至与真实图像难以区分。