Abstract
The growing availability of public multi-domain medical image datasets enables training omnipotent image-to-image (I2I) translation models. However, integrating diverse protocols poses challenges in domain encoding and scalability. Therefore, we propose the "every domain all at once" I2I (EVA-I2I) translation model using DICOM-tag-informed contrastive language-image pre-training (DCLIP). DCLIP maps natural language scan descriptions into a common latent space, offering richer representations than traditional one-hot encoding. We develop the model using seven public datasets with 27,950 scans (3D volumes) for the brain, breast, abdomen, and pelvis. Experimental results show that our EVA-I2I can synthesize every seen domain at once with a single training session and achieve excellent image quality on different I2I translation tasks. Results for downstream applications (e.g., registration, classification, and segmentation) demonstrate that EVA-I2I can be directly applied to domain adaptation on external datasets without fine-tuning and that it also enables the potential for zero-shot domain adaptation for never-before-seen domains.