Three-round learning strategy based on 3D Deep Convolutional GANs for Alzheimer’s disease staging
Bibliographic record
Abstract
Abstract The accurate diagnosis of Alzheimer's disease (AD) and its early stage is essential for early dementia detection and treatment planning. For 2D Convolutional neural networks (CNNs), the networks pre-trained on ImageNet always outperformed the models trained from scratch. 3D CNN is easily to lead to an overfitting problem due to the lack of 3D neuroimage datasets. To alleviate the overfitting caused by small to medium-sized datasets, a three-round learning strategy based on 3D Deep Convolutional Generative Adversarial Networks (DCGAN) is used for staging the spectrum of AD. In the first round, the common features of sMRI were trained in the context of 3D DCGAN by unsupervised learning. In the second round, the pre-trained discriminator (D) of the DCGAN with residual architecture was transferred and fine-tuned for the classification task between AD and cognitive normal (CN). In the last round of the transfer learning approach, the weights learned in the AD versus CN classification task were transferred to MCI diagnosis. The proposed model attained accuracies of 92.8 %, 78.1 %, and 76.4 % in the classification of AD versus CN, AD versus MCI, and MCI versus CN, respectively. The experimental results demonstrate that our proposed model has a good classification performance on staging the AD spectrum with small to medium-sized datasets, compared with other state-of-the-art studies.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".