Deep Learning Framework Based on Generative AI for Medical Image Analysis

Application of deep machine learning algorithms for diagnosing and providing treatment plans is a considerably vital and hopeful domain of concern which can significantly help clinicians. Deep learning is heavily reliant on large amounts of data. Medical data sets are a bit complex to analyse as compared to natural images, as they are relatively of bad quality (as a result of multiple image acquisition artefacts) and infrequently public because of personal privacy restrictions relating to the sharing of patient data. The generation of substitute/synthetic images can save the day, where the synthetic images can have improved quality without any noisy artefacts, have zero privacy concerns as there are no individuals in such images, and synthetic images can be distributed publicly without any issues. This paper presents a novel computational framework based on generative artificial intelligence for creating synthetic or surrogate medical images for diverse downstream medical image analytics tasks, specific for analyzing a large corpus of radiology images across multimodality inputs, especially:diagnosis, tracking, and treatment of complex diseases, with an extensive focus on medical image analytics within the radiation oncology or cancer domain.