Domain specific transfer learning and classifier chains in Alzheimer's disease detection using 3D convolutional neural networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines different configurations of deep convolutional neural networks (CNNs) and the effect of using domain-specific transfer learning for distinguishing Alzheimer’s Disease and Mild Cognitive Impairment from normal controls. The data used to train our models was provided by ADNI and included 1,118 3D FDG-PET scans in total. We train a binary and a multiclass classifier, as well as chains of binary classifiers, for consecutive multiclass classification. Two chains were trained with different orders: chain A classified cognitively normal (CN) vs. non-CN, followed by Alzheimer’s disease (AD) vs. mild cognitive impairment (MCI). Classifier chain B classified AD vs. non-AD first, followed by MCI vs. CN. All classifiers were trained with and without the use of domain-specific transfer learning, using weights from Med3D. All models achieve comparable performance to the state-of-the-art. Classifier chain A even achieved superior performance with an accuracy of 96%, F1 score of 95% and AUROC of 99%. Using domain-specific transfer learning resulted in worse performance among the majority of the models, producing decreases in accuracy of up to 55%. These results show the potential of binary classifier chains and open some questions about the use of domain-specific transfer learning. • Binary classifiers outperform multiclass 3D CNNs in performance. • Chaining binary classifiers for multiclass scenarios improves performance. • The use of domain-specific transfer learning should be evaluated critically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it