A Dual-Discriminator Fourier Acquisitive GAN for Generating Retinal Optical Coherence Tomography Images
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
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.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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