Transclaw U-Net: Claw U-Net With Transformers for Medical Image Segmentation
Bibliographic record
Abstract
In medical image analysis, the long-range spatial features are often not accurately obtained by the traditional convolutional neural networks. Hence, we propose a TransClaw U-Net network structure. The transformer part is added after three convolution operations to fuse shallow features extracted by convolution operations for maximally encoding the long-range spatial features between patches. The “Claw” in TransClaw U-net means that we add the bottom upsampling part to retain the deepest feature information for detail segmentation. In addition, the modified three-channel global attention mechanism to blend the outputs of three channels (the encoding part, the bottom upsampling part and the decoding part) to effectively extract image contours. The experimental results on Synapse Multi-organ Segmentation Dataset show that TransClaw U-Net performs better than other networks. The results of ablation experiments prove the effectiveness of the three improved components of the network and influence of input image size and skip-connection numbers on network performance. The source code will be publicly available once the paper is accepted.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".