Pediatric brain tumor classification using deep learning on MR images with age fusion
Bibliographic record
Abstract
Abstract Purpose To implement and evaluate deep learning-based methods for the classification of pediatric brain tumors (PBT) in magnetic resonance (MR) data. Methods A subset of the “Children’s Brain Tumor Network” dataset was retrospectively used (n = 178 subjects, female = 72, male = 102, NA = 4, age range [0.01, 36.49] years) with tumor types being low-grade astrocytoma (n = 84), ependymoma (n = 32), and medulloblastoma (n = 62). T1w post-contrast (n = 94 subjects), T2w (n = 160 subjects), and apparent diffusion coefficient (ADC: n = 66 subjects) MR sequences were used separately. Two deep learning models were trained on transversal slices showing tumor. Joint fusion was implemented to combine image and age data, and 2 pre-training paradigms were utilized. Model explainability was investigated using gradient-weighted class-activation mapping (Grad-CAM), and the learned feature space was visualized using principal component analysis (PCA). Results The highest tumor-type classification performance was achieved when using a vision transformer model pre-trained on ImageNet and fine-tuned on ADC images with age fusion (Matthews correlation coefficient [MCC]: 0.77 ± 0.14, Accuracy: 0.87 ± 0.08), followed by models trained on T2w (MCC: 0.58 ± 0.11, Accuracy: 0.73 ± 0.08) and T1w post-contrast (MCC: 0.41 ± 0.11, Accuracy: 0.62 ± 0.08) data. Age fusion marginally improved the model’s performance. Both model architectures performed similarly across the experiments, with no differences between the pre-training strategies. Grad-CAMs showed that the models’ attention focused on the brain region. PCA of the feature space showed greater separation of the tumor-type clusters when using contrastive pre-training. Conclusion Classification of PBT on MR images could be accomplished using deep learning, with the top-performing model being trained on ADC data, which radiologists use for the clinical classification of these tumors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".