Effects of Adam Optimizer Variants on Brain Tumor Segmentation Task
Bibliographic record
Abstract
In medical image analysis, accurately segmenting brain tumors is still very challenging, motivating researchers to explore advanced deep-learning methods.While U-Net models have produced promising results, improving their performance through optimized training techniques is still necessary.Given that Adam is commonly used as the default optimizer in such tasks, our study explores the impact of different Adam optimizer variants on U-Net performance using the well-known BraTS 2020 dataset.We evaluated Adam, AdamW, Adagrad, Adamax, Adafactor, and RMSprop optimizers, comparing their performance using key metrics such as training loss, validation loss, F-score, Intersection over Union (IoU), precision, and recall.The obtained results show that Adamax achieves the highest F-score (0.8120) and IoU score, demonstrating superior performance in segmenting tumor regions in medical images; AdamW also showed strong results with lower training and validation losses, as well as good precision and recall, highlighting its efficiency and accuracy.These findings emphasize the importance of selecting the right optimizer for Li-Net-based brain tumor segmentation and encourage further exploration into optimized training strategies in medical image analysis.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".