Authors: Yalda Zafari-Ghadim, Essam A. Rashed, Mohamed Mabrok

Published on: March 27, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2403.18637

Summary

• What is new: This paper reviews the latest Transformer-based architectures for stroke segmentation, showing how these models adapted from language processing can enhance medical image analysis.
• Why this is important: Accurate and timely stroke diagnosis is critical for effective treatment, yet traditional diagnostic tools struggle with efficiency and precision.
• What the research proposes: The research explores how Transformer-based architectures can be applied to improve stroke segmentation in medical imaging, offering more accurate diagnostics.
• Results: The reviewed approaches demonstrate improved accuracy in stroke segmentation, albeit with varying degrees of computational efficiency and performance.

Technical Details

Technological frameworks used: Transformer-based architectures

Models used: Various Transformer models adapted for medical imaging

Data used: Medical images for stroke diagnosis

Potential Impact

Healthcare providers, medical imaging companies, AI-driven diagnostics tools

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