High-resolution conditional MR image synthesis through the PACGAN framework
Bibliographic record
Abstract
Deep learning algorithms trained on medical images often encounter limited data availability, leading to overfitting and imbalanced datasets. Synthetic datasets can address these challenges by providing a priori control over dataset size and balance. In this study, we present PACGAN (Progressive Auxiliary Classifier Generative Adversarial Network), a proof-of-concept framework that effectively combines Progressive Growing GAN and Auxiliary Classifier GAN (ACGAN) to generate high-quality, class-specific synthetic medical images. PACGAN leverages latent space information to perform conditional synthesis of high-resolution brain magnetic resonance (MR) images, specifically targeting Alzheimer's disease patients and healthy controls. Trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, PACGAN demonstrates its ability to generate realistic synthetic images, which are assessed for quality using quantitative metrics. The ability of the generator to perform proper target synthesis of the two classes was also assessed by evaluating the performance of the pre-trained discriminator when classifying real unseen images, which achieved an area under the receiver operating characteristic curve (AUC) of 0.813, supporting the ability of the model to capture the target characteristics of each class. The pre-trained models of the generator and discriminator, together with the source code, are available in our repository: https://github.com/aiformedresearch/PACGAN .
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".