A U-Net Baseline for Left Atrial Tumor Segmentation: Performance Analysis and Limitations
Why this work is in the frame
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Bibliographic record
Abstract
Accurate and robust automated segmentation of Left Atrial (LA) tumors is essential for clinical diagnosis and treatment planning. Due to the inherent challenges in cardiac imaging, such as low tumor-to-background contrast and subtle boundaries, high-precision segmentation remains difficult. This study proposes and evaluates a standard 2D U-Net architecture for effective LA tumor segmentation. We address class imbalance using the Dice Loss function and enhance generalization through critical data augmentation, including elastic deformation. Evaluated on an independent cardiac MRI dataset, the U-Net model achieves a Dice Similarity Coefficient (DSC) of 0.8145, demonstrating its strong capability as a reliable baseline for this challenging task.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it