  Recent escalation in the field of computer vision underpins a huddle of
algorithms with the magnificent potential to unravel the information contained
within images. These computer vision algorithms are being practised in medical
image analysis and are transfiguring the perception and interpretation of
Imaging data. Among these algorithms, Vision Transformers are evolved as one of
the most contemporary and dominant architectures that are being used in the
field of computer vision. These are immensely utilized by a plenty of
researchers to perform new as well as former experiments. Here, in this article
we investigate the intersection of Vision Transformers and Medical images and
proffered an overview of various ViTs based frameworks that are being used by
different researchers in order to decipher the obstacles in Medical Computer
Vision. We surveyed the application of Vision transformers in different areas
of medical computer vision such as image-based disease classification,
anatomical structure segmentation, registration, region-based lesion Detection,
captioning, report generation, reconstruction using multiple medical imaging
modalities that greatly assist in medical diagnosis and hence treatment
process. Along with this, we also demystify several imaging modalities used in
Medical Computer Vision. Moreover, to get more insight and deeper
understanding, self-attention mechanism of transformers is also explained
briefly. Conclusively, we also put some light on available data sets, adopted
methodology, their performance measures, challenges and their solutions in form
of discussion. We hope that this review article will open future directions for
researchers in medical computer vision.
