Transformer Models in Medical Imaging: An Extensive Analysis of Advancements, Uses, and Prospects

Transformer models have completely changed the field of natural language processing, and medical imaging is starting to pay more and more attention to their prospective uses. A thorough analysis of the advancements uses and potential future developments of transformer models in the field of medical imaging is given in this survey article. We examine transformers’ basic design, their distinct benefits over conventional convolutional neural networks, and the most recent developments that have made it easier for them to be used in medical imaging applications. Images’ roles in prognostic and diagnostic workflows, as well as their classification, segmentation, and synthesis, are among the key applications that are covered. Additionally, the study outlines ongoing research targeted at resolving the obstacles and limits related to transformer models, including computational complexity and the requirement for huge annotated datasets. In conclusion, we offer a prognosis of forthcoming patterns and the possible influence of transformer models on the wider domain of medical imaging. We underscore the significance of interdisciplinary cooperation and the amalgamation of novel technologies to completely actualize their potential.