Effective data augmentation for brain tumor segmentation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This research is to propose a training strategy for 2D U‐Net is proposed that uses selective data augmentation technique to overcome the class imbalance issue. This also helps in generating synthetic data for training which improves the generalization capabilities of the segmentation network. The training data are prepared with random sampling to further reduce the class imbalance. The post‐processing stage is used to decrease the outliers in the final output. The performance of the proposed solution is tested on the online leaderboard. The results achieved on the validation set of Brain Tumor Segmentation 2019 dataset were 0.79, 0.89, and 0.8 for enhancing tumor (ET), whole tumor (WT), and core tumor (CT) respectively. The part of the training set is also evaluated locally, and the results show the effectiveness of using selective data augmentation and random sampling. The multi‐view fusion improved the robustness and overall dice scores.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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