An Exploration: Alzheimer’s Disease Classification Based on Spectral Matching of Shape Features
Bibliographic record
Abstract
In this paper, we use structural deformations to classify dementia patients. Firstly, surface meshes are recovered from MRI segmented hippocampal, and node-to-node interactions between all the surface meshes are constructed using a spectral matching approach. Then, to learn the low-dimensional feature representation, an enhanced version of the variational auto-encoder (VAE) is given to the vertex coordinates of the surface meshes. We describe a new strategy for increasing variational autoencoder performance (VAE). We designed a generative adversarial training (GAN) technique to train the VAE to generate realistic medical images and apply the deep feature consistency principle, ensuring that the VAE output and its related input images have identical features. A discriminator with a SoftMax layer is concurrently trained to distinguish people with Alzheimer's from healthy people. Studies on the ADNI dataset show that the proposed method can distinguish normal people from early AD/NC and AD/EMCI classes with low computational time and higher accuracy that outperforms the support vector machine (SVM) baseline approach. All the simulation results are carried out with the Anaconda tool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".