Work

Dual-Generator and Dynamically Fused Discriminators Adversarial Network to Create Synthetic Coronary Optical Coherence Tomography Images for Coronary Artery Disease Classification

Year: 2025

Type: article

Abstract: Deep neural networks have led to a substantial increase in multifaceted classification tasks by making use of large-scale and diverse annotated datasets. However, diverse optical coherence tomography (OCT) datasets in cardiovascular imaging remain an uphill task. This research focuses on improving the diversity and generalization ability of augmentation architectures while maintaining the baseline classification accuracy for coronary atrial plaques using a novel dual-generator and dynamically fused discriminator conditional generative adversarial network (DGDFGAN). Our method is demonstrated on an augmented OCT dataset with 6900 images. With dual generators, our network provides the diverse outputs for the same input condition, as each generator acts as a regulator for the other. In our model, this mutual regularization enhances the ability of both generators to generalize better across different features. The fusion discriminators use one discriminator for classification purposes, hence avoiding the need for a separate deep architecture. A loss function, including the SSIM loss and FID scores, confirms that perfect synthetic OCT image aliases are created. We optimize our model via the gray wolf optimizer during model training. Furthermore, an inter-comparison and recorded SSID loss of 0.9542 ± 0.008 and a FID score of 7 are suggestive of better diversity and generation characteristics that outperform the performance of leading GAN architectures. We trust that our approach is practically viable and thus assists professionals in informed decision making in clinical settings.

Source: Optics

DOI: 10.3390/opt6030038

Authors: Junaid Zafar, Faisal Sharif, Haroon Zafar

Institutions: Government College University, Lahore, Ollscoil na Gaillimhe – University of Galway, University Hospital Galway, Taighde Éireann - Research Ireland

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,448