# Frequency-Domain Decomposition Translation

Medical image synthesis is often treated as filling in a missing modality, yet a realistic image need not help the downstream task. Across five public datasets, a training framework ties synthesis to task learning: the translator is constrained during training by image-adaptive frequency consistency on structure and detail, and the downstream network receives teacher features only when a teacher using synthetic images beats the student by a task-specific margin on labelled samples. Translation fidelity improved for CycleGAN, CUT and SynDiff unpaired translators, with gains in brain-tumour segmentation and lesion centre-point detection, and the training-time components are removed at inference.

Medical image synthesis is often treated as filling in a missing modality, yet a realistic image need not help the downstream task. Across five public datasets, a training framework ties synthesis to task learning: the translator is constrained during training by image-adaptive frequency consistency on structure and detail, and the downstream network receives teacher features only when a teacher using synthetic images beats the student by a task-specific margin on labelled samples. Translation fidelity improved for CycleGAN, CUT and SynDiff unpaired translators, with gains in brain-tumour segmentation and lesion centre-point detection, and the training-time components are removed at inference.