Core Concepts
Transformers have revolutionized stroke segmentation by effectively capturing complex spatial information within medical images.
Abstract
The content provides a comprehensive review of Transformers-based architectures for stroke segmentation. It covers the challenges in stroke diagnosis, the evolution of deep learning in medical image analysis, and the application of Transformers in capturing spatial information. The review categorizes existing literature, analyzes various approaches, and discusses the strengths and limitations of Transformer-based methods. It also explores potential avenues for future research and development.
The content is structured as follows:
- Introduction to Stroke and Imaging Modalities
- Fundamentals of Transformers
- Vision Transformer Pipeline
- Adaptations for Medical Image Analysis
- Performance Evaluation for Stroke Segmentation
- Earlier Approaches for Stroke Segmentation
- Transformer-Based Architectures for Stroke Segmentation
- Datasets for Stroke Segmentation
Stats
Stroke remains a significant global health concern, impacting over 100 million people globally.
MRI offers excellent soft tissue contrast for the brain.
The ISLES 2015 dataset consisted of 64 sub-acute ischemic cases.
The ISLES 2018 dataset included information from 103 acute ischemic cases.
The ATLAS v2.0 dataset contained data from 1,271 cases.
Quotes
"Transformers have gained widespread attention in the computer vision community."
"Hybrid Transformer-CNN models offer flexibility in capturing both local and global information."
"Transformers have proven their effectiveness when utilized as the upsampling components within the decoder section."