A Recent Survey of Vision Transformers for Medical Image Segmentation
CoRR(2023)
Abstract
Medical image segmentation plays a crucial role in various healthcare
applications, enabling accurate diagnosis, treatment planning, and disease
monitoring. In recent years, Vision Transformers (ViTs) have emerged as a
promising technique for addressing the challenges in medical image
segmentation. In medical images, structures are usually highly interconnected
and globally distributed. ViTs utilize their multi-scale attention mechanism to
model the long-range relationships in the images. However, they do lack
image-related inductive bias and translational invariance, potentially
impacting their performance. Recently, researchers have come up with various
ViT-based approaches that incorporate CNNs in their architectures, known as
Hybrid Vision Transformers (HVTs) to capture local correlation in addition to
the global information in the images. This survey paper provides a detailed
review of the recent advancements in ViTs and HVTs for medical image
segmentation. Along with the categorization of ViT and HVT-based medical image
segmentation approaches we also present a detailed overview of their real-time
applications in several medical image modalities. This survey may serve as a
valuable resource for researchers, healthcare practitioners, and students in
understanding the state-of-the-art approaches for ViT-based medical image
segmentation.
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