Biomedical images generation for data augmentation using generative adversarial networks

Medical application development is an important direction in the healthcare. However, this task requires accurate annotations of biomedical images, which are often sparse and difficult to obtain. Neural networks can be used to generate such images to increase the amount of data significantly.One of the most effective image generation methods is the use of generative adversarial networks (GANs), which can help in data augmentation and improve the quality of medical image segmentation. This technique is especially useful in cases where access to real data is limited or when a large amount of data is required to train machine or deep learning models.The aim of the research is to create the biomedical image generation method by using GAN for data augmentation.