Revisiting model scaling with a U-net benchmark for 3D medical image segmentation
Bibliographic record
Abstract
Are larger models always better for 3D medical image segmentation? Despite the widespread adoption of 3D U-Net in various medical imaging tasks, this critical question remains underexplored. To challenge the common assumption, we systematically benchmark 18 U-Net variants-adjusting resolution stages, depth, and width-across 42 diverse public datasets. Our findings reveal that the answer is no: optimal architectures are highly task-specific, with smaller models often performing competitively. Specifically, we identify three key insights: (1) increasing resolution stages provides limited benefits for datasets with larger voxel spacing; (2) deeper networks offer limited advantages for anatomically complex shapes; and (3) wider networks provide minimal advantages for tasks with limited segmentation classes. Based on these insights, we provide practical guidelines for optimizing U-Net architectures according to dataset characteristics. Our findings highlight the limitations of the"bigger is better"paradigm while establishing a framework for balancing performance and computational efficiency in 3D medical image segmentation tasks.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".