Application of visual transformer in renal image analysis

Area of Science:

• Artificial Intelligence
• Medical Imaging
• Nephrology

Background:

• Deep Self-Attention Networks (Transformers), initially used in natural language processing, excel at capturing long-distance dependencies.
• Transformers complement Convolutional Neural Networks (CNNs), leading to their increasing application in medical image analysis, particularly for kidney imaging.
• Renal image processing is a rapidly evolving research area with significant potential for AI-driven advancements.

Purpose of the Study:

• To outline the characteristics of Transformer network models.
• To summarize the applications of Transformer-based models in various renal image processing tasks.
• To compare Transformer-based approaches with traditional CNN-based algorithms in renal image analysis.

Main Methods:

• Review of Transformer network architecture and its suitability for medical imaging.
• Synthesis of current research on Transformer applications in renal image segmentation, classification, detection, electronic medical records, and decision-making systems.
• Comparative analysis of Transformer and CNN performance in renal image processing.

Main Results:

• Transformers demonstrate significant potential in renal image segmentation, classification, and detection tasks.
• The complementary nature of Transformers with CNNs offers enhanced capabilities for complex renal image analysis.
• Analysis highlights the advantages and disadvantages of Transformer models compared to CNNs in this domain.

Conclusions:

• Transformer models represent a promising frontier in renal image processing, offering novel solutions for various analytical challenges.
• The integration of Transformers is expected to drive innovation in kidney image analysis, improving diagnostic accuracy and treatment planning.
• This review provides a valuable reference for researchers and practitioners exploring the future development of AI in renal imaging.