Healthy Counterfactual Generation via Diffusion Inpainting for Mammography Classification
The method generates realistic, healthy versions of mammograms by training a Diffusion Model only on healthy images and using RePaint to fill in areas where lesions were annotated. This technique enriches the training data, leading to improved sensitivity in breast cancer detection models across various architectures, especially at an 80% specificity threshold.