Geometric Deep Learning on Anatomical Meshes for the Prediction of\n Alzheimer's Disease
Bibliographic record
Abstract
Geometric deep learning can find representations that are optimal for a given\ntask and therefore improve the performance over pre-defined representations.\n While current work has mainly focused on point representations, meshes also\ncontain connectivity information and are therefore a more comprehensive\ncharacterization of the underlying anatomical surface.\n In this work, we evaluate four recent geometric deep learning approaches that\noperate on mesh representations.\n These approaches can be grouped into template-free and template-based\napproaches, where the template-based methods need a more elaborate\npre-processing step with the definition of a common reference template and\ncorrespondences.\n We compare the different networks for the prediction of Alzheimer's disease\nbased on the meshes of the hippocampus.\n Our results show advantages for template-based methods in terms of accuracy,\nnumber of learnable parameters, and training speed.\n While the template creation may be limiting for some applications,\nneuroimaging has a long history of building templates with automated tools\nreadily available.\n Overall, working with meshes is more involved than working with simplistic\npoint clouds, but they also offer new avenues for designing geometric deep\nlearning architectures.\n
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".