OCTA-based AMD Stage Grading Enhancement via Class-Conditioned Style Transfer

Optical Coherence Tomography Angiography (OCTA) is a promising diagnostic tool for age-related macular degeneration (AMD), providing non-invasive visualization of sub-retinal vascular networks. This research explores the effectiveness of deep neural network (DNN) classifiers trained exclusively on OCTA images for AMD diagnosis. To address the challenge of limited data, we combine OCTA data from two instruments-Heidelberg and Optovue-and leverage style transfer technique, CycleGAN, to convert samples between these domains. This strategy introduces additional content into each domain, enriching the training dataset and improving classification accuracy. To enhance the CycleGAN for downstream classification tasks, we propose integrating class-related constraints during training, which can be implemented in either supervised or unsupervised manner with a pretrained classifier. The experimental results demonstrate that the proposed class-conditioned CycleGAN is effective and elevates DNN classification accuracy in both OCTA domains.