A Dual-Branch Unsupervised Network with Wavelet Transform and Style Consistency for CT to MRI Image Generation <sup>*</sup>
Bibliographic record
Abstract
Medical image generation plays an important role in clinical applications, particularly in modality translation tasks such as CT-to-MRI synthesis. Compared with supervised methods that rely on paired datasets, unsupervised image generation is more practical due to its lower data requirements. However, existing unsupervised methods often suffer from insufficient high-frequency detail preservation and poor style consistency. Although wavelet transform offers promising multi-scale representation capabilities, effectively integrating it into deep generative models remains challenging. To address these issues, we propose WDS-Net (Wavelet-based Dual-branch Style-consistent Network). In the content encoder, wavelet decomposition is used to separate the image into low-frequency structural and high-frequency detail components, which are processed through a dual-branch architecture and then fused to enhance content representation. A multi-scale feature extraction mechanism is employed in the style encoder, and layer-wise style injection is applied during decoding to improve style consistency. Experimental results demonstrate that WDS-Net can generate high-quality MRI images under unpaired training conditions and achieves robust performance even with limited data. Evaluations on both public and clinical datasets confirm that WDS-Net outperforms existing methods in detail preservation and style consistency, showing strong potential for real-world clinical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".