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Transformers-based architectures for stroke segmentation: A review
Stroke remains a significant global health concern, necessitating precise and efficient diagnostic tools for timely intervention and improved patient outcomes.
Brain Stroke Segmentation Using Deep Learning Models: A Comparative Study
In this study, we selected four types of deep models that were recently proposed and evaluated their performance for stroke segmentation: a pure Transformer-based architecture (DAE-Former), two advanced CNN-based models (LKA and DLKA) with attention mechanisms in their design, an advanced hybrid model that incorporates CNNs with Transformers (FCT), and the well- known self-adaptive nnUNet framework with its configuration based on given data.
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