Deep Models for Stroke Segmentation: Do Complex Architectures Always Perform Better?

<p dir="ltr">The accurate segmentation of stroke lesions is crucial for the diagnosis and treatment of stroke patients, as it provides spatial information about affected brain regions and the extent of damage. While conventional manual techniques are time-consuming and prone to error...

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Bibliographic Details
Main Author:	Ahmed Soliman (4591621) (author)
Other Authors:	Yalda Zafari-Ghadim (22282849) (author), Yousif Yousif (22282852) (author), Ahmed Ibrahim (1771174) (author), Amr Mohamed (3508121) (author), Essam A. Rashed (11949249) (author), Mohamed A. Mabrok (22282855) (author)
Published:	2024
Subjects:	Biomedical and clinical sciences Neurosciences Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Convolutional neural networks deep learning nnU-Net stroke segmentation vision Transformer Image segmentation Transformers Computer architecture Stroke (medical condition) Feature extraction Data models Computer vision Decoding Accuracy
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Deep Models for Stroke Segmentation: Do Complex Architectures Always Perform Better?

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