Applying nnU-Net to the KiTS19 Grand Challenge
Why this work is in the frame
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Bibliographic record
Abstract
U-Net, conceived in 2015, is making a resurgence in medical semantic segmentation tasks. This comeback is largely thanks to the excellent performance of nnU-Net in recent competitions. nnU-Net generalizes well, as proven by its first-place finish in the Medical Segmentation Decathalon. Notably, nnU-Net focuses on the training process rather than algorithmic improvements, and can often beat more complex algorithms. This paper shows the results of applying nnU-Net to the KiTS19 Kidney Segmentation Grand Challenge. Each of the 5 cross-validation training folds achieves good results, with scores nearing or exceeding 0.9 after approximately 500 epochs per fold.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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