Jednotky: SwinUNeLCsT: Global–local spatial representation learning with hybrid CNN–transformer for efficient tuberculosis lung cavity weakly supervised semantic segmentation

SwinUNeLCsT: Global–local spatial representation learning with hybrid CNN–transformer for efficient tuberculosis lung cavity weakly supervised semantic segmentation

Radiological diagnosis of lung cavities (LCs) is the key to identifying tuberculosis (TB). Conventional deep learning methods rely on a large amount of accurate pixel-level data to segment LCs. This process is timeconsuming and laborious, especially for those subtle LCs. To address such challenges,...

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Hlavní autoři:	Zhuoyi, Tan, Hizmawati, Madzin, Bahari, Norafida, Rahmita, Wirza OK Rahmat, Fatimah, Khalid, Puteri, Suhaiza Sulaiman
Médium:	Článek
Jazyk:	English
Vydáno:	Elsevier 2024
On-line přístup:	http://psasir.upm.edu.my/id/eprint/111381/1/SwinUNeLCsT.pdf
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http://psasir.upm.edu.my/id/eprint/111381/1/SwinUNeLCsT.pdf

SwinUNeLCsT: Global–local spatial representation learning with hybrid CNN–transformer for efficient tuberculosis lung cavity weakly supervised semantic segmentation

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