Improving Prenatal Alcohol Exposure classification using Data Augmentation in 3D Convolutional Neural Networks
Bibliographic record
Abstract
Prenatal alcohol exposure (PAE) is the consumption of alcohol by a mother during pregnancy, potentially leading to a range of developmental and behavioral problems in the child. Early detection is vital to mitigate these problems. In addition to clinical assessment, magnetic resonance (MR) scans offers a non-invasive method to examine the brain's detailed structure, enabling researchers to explore subtle anatomical variations associated with PAE. Machine learning, particularly$3\mathrm{D}$Convolutional Neural Networks (CNNs), serves as a powerful tool to automatically identify patterns in the children's brains. However, the limited size of available PAE data creates a significant bottleneck for developing effective models. To address this, our goal is to explore data augmentation techniques to artificially expand the dataset, thereby improving model performance and generalizability. We assessed three rates of data augmentation$(1x,\ 3x,\ 6x)$in six 3D CNN models with increasing architectural complexity. Adding data augmentation significantly improved validation accuracy$(3x$and$6x \quad vs \quad1x)$, but no significant difference was found between$3x$and$6x$. Models with fewer layers and balanced number of parameters had higher accuracy. In conclusion, simpler CNN architectures generalized better, and data augmentation aids model convergence, though benefits plateau beyond$3x$augmentation. Future work includes adding interpretability modules to identify brain differences among participants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".