## 1 Introduction  
In recent years, Generative Adversarial Networks (GANs) have been increasingly used in medical diagnostic image analysis. In the last four years, significant work has been devoted to the detection of COVID-19 [1, 2] as well as its application in medical diagnostics for detecting various benign and malignant tumors [3]. A key application of GANs in medical imaging is image augmentation, a crucial technique used to artificially expand limited datasets and enhance the generalization capability of deep learning models. In the medical domain, where obtaining large annotated datasets is challenging due to privacy concerns, acquisition costs, and the need for expert annotations, augmentation techniques play a vital role in overcoming data scarcity [4, 5]. GANs are used to generate synthetic medical images, which help supplement datasets when high-quality annotated medical images are lacking. These generated images complement existing data and enable the development of increasingly reliable diagnostic models. In the work [6], the authors explain six different applications of GANs in medical image analysis: medical image segmentation; classification; reconstruction; detection; synthesis; noise reduction. Endoscopic examinations serve as the gold standard for diagnosing and assessing a wide range of gastrointestinal diseases. These real-time video examinations provide high-definition visualization of the GI tract's interior, revealing specific visual signatures that are critical for accurate diagnosis. For instance, polyps appear as mucosal outgrowths with distinctive color and surface patterns that may develop into cancer if untreated. Inflammatory conditions display equally characteristic features--esophagitis presents as red mucosal tongues projecting from the Z-line, while ulcerative colitis exhibits bleeding, swelling, and ulceration with white fibrin coating. The precise assessment of disease severity and sub-classification directly influences treatment decisions and follow-up care protocols. Despite their clinical value, these examinations require both expensive equipment and highly trained personnel. The development of computer-aided detection and diagnostic systems could potentially democratize access to expert-level analysis, reduce inequalities in care delivery, optimize the use of limited medical resources, and allow clinicians to dedicate more time to patient care rather than documentation [5]. Early detection of various cancers (colorectal cancer CRC, lung cancer and others) through screening has demonstrated significant benefits, with studies showing steady declines in both CRC incidence and mortality rates. This decline is attributed to improved risk factor management, early cancer detection through screening programs, precancerous polyp removal via colonoscopy, and advances in surgical and treatment approaches [7]. Most colorectal cancers develop through either the adenoma-carcinoma sequence or from sessile serrated lesions, presenting valuable opportunities for cancer prevention through timely intervention [5;7]. Despite significant advancements in medical imaging technologies, substantial challenges persist in developing automated analysis systems for diagnostic applications. Further improvement of deep learning models in medical image analysis is majorly bottleneck by the lack of large-sized and well-annotated datasets [8]. Deep neural network architectures require extensive training data containing all possible variations to achieve high generalization ability and robustness [9]. However, medical images are notoriously scarce due to several interrelated factors: insufficient patients for certain diseases, patients' unwillingness to allow the use of their images, lack of appropriate medical equipment, and inability to obtain images meeting desired criteria [8;9]. Even when sufficient images are acquired, proper annotation requires specialized domain knowledge from medical professionals, making the process both time-consuming and expensive [8].