{'fraction_non_alphanumeric': 0.015873015873015872, 'fraction_numerical': 0.0006901311249137336, 'mean_word_length': 5.775700934579439, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Leveraging Adversarial Training for Efficient Retinal Vessel Segmentation

Detection and diagnosis of retinal-related diseases is a crucial problem in the field of ophthalmology. Segmentation of blood vessels forms the basis for the identification of several ailments. Manual inspection of the retinal blood vessels necessitates utmost expertise and focus. Moreover, it is a strenuous and cumbersome process. It becomes imperative to develop a reliable automatic retinal vessel segmentation algorithm that can reduce the burden on the medical fraternity. This paper leverages adversarial training techniques to deploy an accurate deep learning architecture for efficacious retinal vessel segmentation. A Conditional Generative Adversarial Network with U-Net architecture as the Generative model and Patch-GAN architecture as the Discriminative model has been developed. Furthermore, Depth-Wise Separable Convolutions have been utilized to ameliorate the efficiency of the architecture. The incorporation of Dice Loss with L1 Loss in the GAN objective has enhanced the proposed architecture's precision in identifying even thin blood vessels in the retina. To allow rapid convergence of the loss function, the NADAM optimizer has been incorporated. Quantitative and qualitative comparison with several state-of-the-art methods indicate the improved performance of the proposed method and establishes the developed architecture's clinical reliability.