TMA-UNet: A Parameter-Efficient Deep Learning Model for Fetal Brain MRI Segmentation
Bibliographic record
Abstract
The segmentation of fetal brain structures in MRI images is crucial for the early diagnosis of neurological abnormalities. However, existing deep learning models suffer from high computational complexity due to the excessive number of learnable parameters, which limits their practical use in clinical settings. This paper introduces TMA-UNet, a novel deep learning architecture designed to reduce the parameter count while maintaining high segmentation accuracy. Experimental results on the Fetal Brain Atlas from Harvard University demonstrate that our model outperforms other models, achieving an average Dice coefficient of 99.02%, IoU of 98.85%, and F1-score of 99.04%. Notably, TMA-UNet achieves this performance with only 1.39 million parameters, a substantial reduction compared to other state-of-the-art segmentation models. This efficiency enhances the model’s feasibility for real-world deployment and establishes a new benchmark for parameter-efficient medical image segmentation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".