Method overview. In the proposed approach, we first trained a diffusion model on both labeled and unlabeled data (if available). In a general setting, unlabeled data may consist of in-distribution or OOD data (for example, from an unseen hospital) for which expert labels are not available. Subsequently, we sampled synthetic images from the diffusion model according to particular specifications (for example, an image of a female individual with pulmonary edema). Finally, we trained a downstream diagnostic model on a combination of the real labeled images and the synthetic images sampled from the diffusion model. The dotted outlines represent synthetic data, while the dashed outlines represent unlabeled data.
