Swef-UNet: Toward an Efficient Pure Transformer-Based Medical Image Segmentation
Bibliographic record
Abstract
Transformers have significantly advanced the field of medical image segmentation. Despite the advancements, challenges remain: notably, the computational intensity of the self-attention mechanism and the nuanced learning of spatial representations within purely Transformer-based frameworks. In response, building on the strength of Transformers, this paper introduces a novel architecture by first, reformulating the self-attention mechanism to incorporate efficient attention, reducing the computational burden without compromising the richness of information captured. This adaptation maintains the core advantages of dot-product attention - capturing complex dependencies within the data - but with a fraction of the memory and computational cost. Second, by employing deep supervision at each decoder layer of our UNet-like network, designed to enhance segmentation accuracy by promoting the development of richer, more discriminative features throughout the network. Together, the adaptation of efficient attention and the implementation of deep supervision offer a balanced solution between computational efficiency and segmentation precision. Our findings reveal the model's capacity to outperform state-of-the-art results.
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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 | 0.003 |
| 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) | 0.004 | 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".