TVNet: Multimodal medical image fusion by dual-branch network with vision transformer and one-shot aggregation
Bibliographic record
Abstract
The task of medical image fusion involves synthesizing complementary information from different modal medical images, which is of very significant for clinical diagnosis. The existing medical image fusion algorithms overly rely on convolution operations and cannot establish long-range dependencies on the source images. This can lead to edge blurring and loss of details in the fused images. Because the Transformer can effectively model long-range dependencies through self-attention, a novel and effective dual-branch feature enhancement network called TVNet is proposed to fuse multimodal medical images. This network combines Vision Transformer and Convolutional Neural Network to extract global context information and local information to preserve detailed textures and highlight the structural characteristics in source images. Furthermore, to extract the multiscale information of images, an enhancement module is used to obtain multiscale characterization information, and the two branches information are efficiently aggregated at the same time. In addition, a hybrid loss function is designed to optimize the fusion results at three levels of structure, feature, and gradient. Experiment results prove that the performance of the proposed fusion network outperforms seven state-of-the-art methods in both subjective visual effects and objective metrics. Our code is available at https://github.com/sineagles/TVNet.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".