Addformer: Alzheimer’s Disease Detection from Structural Mri Using Fusion Transformer
Bibliographic record
Abstract
Alzheimer’s disease is the most prevalent neurodegenerative disorder characterized by degeneration of the brain. It is classified as a brain disease causing dementia that presents with memory loss and cognitive impairment. Experts primarily use brain imaging and other tests to rule out the disease. To automatically detect Alzheimer’s patients from healthy controls, this study adopts the vision transformer architecture, which can effectively capture the global or long-range relationship of image features. To further enhance the network’s performance, frequency and image domain features are fused together since MRI data is acquired in the frequency domain before being transformed to images. We train the model with selected coronal 2D slices to leverage the transfer learning property of pre-training the network using ImageNet. Finally, the majority voting of the coronal slices of an individual subject is used to generate the final classification score. Our proposed method has been evaluated on the publicly available benchmark dataset ADNI. The experimental results demonstrate the advantage of our proposed approach in terms of classification accuracy compared with that of the state-of-the-art methods. Our code is available at https://github.com/rkushol/ADDFormer.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.004 | 0.003 |
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".