Exploring the Impact of Supervised Multimodal Learning on the Performance and Explainability of Pediatric Brain Tumor Molecular Diagnosis
Bibliographic record
Abstract
Pediatric Low-grade Glioma (pLGG) is one of the most common brain tumors in children, and identifying its genetic markers (molecular diagnosis) is crucial for tumor prognosis and targeted treatment planning. Convolution Neural Networks (CNNs) have shown strong performance in predicting these genetic markers from brain magnetic resonance imaging (MRI) data. Nonetheless, most CNN-based architectures rely on tumor segmentation masks to specify regions of interest (ROIs) in the image to achieve high performance, which are labor-intensive and costly to obtain. In this work, we propose a supervised multimodal learning framework that integrates radiology reports, readily available in most medical imaging datasets, with entire MRI scans, eliminating the need for providing segmentation masks. In addition to improving the performance of pLGG molecular diagnosis, we examine the effect of this framework on the alignment between predictive imaging features and domain knowledge, as a measure of model explainability. Our results indicate a considerable improvement in the Area Under the Receiver Operating Curve compared to an MRI-only CNN model (0.863 vs 0.79), highlighting the value of radiology reports in enhancing CNN performance. However, the lower dice score between model attention maps and segmentation masks suggests that supervised training may not optimally integrate the two modalities. These findings signify the potential of multimodal learning in pLGG molecular diagnosis while underscoring the need for future exploration of other approaches, e.g., self-supervised learning, to improve the alignment between learnt features and clinical reasoning.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".