Medical Image Synthesis

Medical image synthesis studies how to create or transform images such as MRI, CT, PET, ultrasound, and pathology scans when scans are missing, costly, or scarce. GANs and VAEs, diffusion and flow-matching models, autoregressive generators, and neural representations are conditioned on anatomy, clinical text, masks, acquisition physics, or patient history to translate modalities, model disease progression, and generate training data. The work matters because synthetic images could reduce acquisition burdens, expand underrepresented datasets, and support tasks such as segmentation, diagnosis, and treatment planning. A central challenge is that visual fidelity does not guarantee preservation of lesions, patient-specific anatomy, or downstream clinical utility, while generalization, privacy, and computational cost also remain important concerns.