A Dual-Discriminator Fourier Acquisitive GAN for Generating Retinal Optical Coherence Tomography Images
Bibliographic record
Abstract
Optical coherence tomography (OCT) images are widely used for clinical examination of the retina. Automatic deep learning-based methods have been developed to classify normal and pathological OCT images. However, lack of the big enough training data reduces the performance of these models. Synthesis of data using generative adversarial networks (GANs) is already known as an efficient alternative to increase the amount of the training data. However, the recent works show that despite high structural similarity between synthetic data and the real images, a considerable distortion is observed in frequency domain. Here, we propose a dual discriminator Fourier acquisitive GAN (DDFA-GAN) to generate more realistic OCT images with considering the Fourier domain similarity in structural design of the GAN. By applying two discriminators, the proposed DDFA-GAN is jointly trained with the Fourier and spatial details of the images and is proven to be feasible with a limited number of training data. Results are compared with popular GANs, namely, DCGAN, WGAN-GP, and LS-GAN. In comparison, Fréchet inception distance (FID) score of 51.30, and Multi Scale Structural Similarity Index Measure (MS-SSIM) of 0.19 indicate superiority of the proposed method in producing images resembling the same quality, discriminative features, and diversity, as the real normal and Diabetic Macular Edema (DME) OCT images. The statistical comparison illustrates this similarity in the spatial and frequency domains, as well. Overall, DDFA-GAN generates realistic OCT images to meet requirements of the training data in automatic deep learning-based methods, used for clinical examination of the retina, and to improve the accuracy of the subsequent measurements.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".