Source-free cross-modality medical image synthesis with diffusion priors
Bibliographic record
Abstract
Cross-modality medical image synthesis plays a critical role in enabling comprehensive multi-modal diagnosis and treatment. However, existing methods are constrained by their reliance on paired or unpaired source-target data, limiting scalability and practical deployment. Generating high-fidelity medical images in a truly source-free setting, where no source-domain data is accessible, remains a significant and underexplored challenge. To fill this gap, we propose Diffusion Prior Synthesis and Optimization (DPSO), a novel source-free, diffusion-based framework that performs cross-modality medical image synthesis using only single-modality target data, without requiring supervision or statistical priors from the source domain. DPSO adopts a decoupled architecture via a Probability Flow ODE (PF-ODE) formulation that separates source encoding from target generation. A general-domain diffusion model maps notional source images into a shared latent space, independent of source-domain supervision. This latent representation is then decoded into the target modality using a PF-ODE solver guided by a target-specific prior. An additional optimization stage, also driven by the target prior, further refines the outputs to enhance fidelity and robustness. Experiments on the IXI Dataset and SynthRAD2023 demonstrate that DPSO achieves competitive performance across diverse cross-modality tasks, comparable to methods using paired or unpaired source data. Notably, DPSO removes the need for source modality data entirely, offering a flexible and scalable solution for truly source-free cross-modality medical image synthesis. Code is available at: https://anonymous.4open.science/r/DPSO-64DF
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".