Vision transformer architecture and applications in digital health: a tutorial and survey

Area of Science:

• Computer Vision
• Artificial Intelligence
• Digital Health

Background:

• Vision Transformers (ViT) represent a significant advancement in image recognition.
• Medical imaging constitutes 90% of data in digital medicine, highlighting the need for efficient analysis tools.
• ViT architecture is crucial for state-of-the-art performance in image-related tasks.

Purpose of the Study:

• To explore the foundational principles of the Vision Transformer (ViT) architecture.
• To detail the diverse applications of ViT in digital health, including medical image analysis and telehealth.
• To provide a strategic roadmap for integrating ViT into digital health systems.

Main Methods:

• Review of Vision Transformer (ViT) architecture fundamentals.
• Analysis of ViT's role in various digital health applications: segmentation, classification, detection, prediction, reconstruction, synthesis, and telehealth (report generation, security).
• Discussion on implementation strategies, challenges, and limitations of ViT in digital health.

Main Results:

• ViT demonstrates significant potential across a wide spectrum of medical imaging tasks.
• Applications span from diagnostic aids (segmentation, classification, detection) to therapeutic tools (reconstruction, synthesis).
• Telehealth benefits include automated report generation and enhanced data security through ViT.

Conclusions:

• Vision Transformers (ViT) are pivotal for advancing digital health and medical image analysis.
• Successful implementation requires addressing ViT's limitations and challenges.
• ViT offers a promising future for AI-driven healthcare solutions.