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 time-consuming and laborious, especially for those subtle LCs. To address such challenges,...

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Bibliographic Details

Main Authors:	Tan, Zhuoyi, Madzin, Hizmawati, Norafida, Bahari, Ok Rahmat, Rahmita Wirza, Khalid, Fatimah, Sulaiman, Puteri Suhaiza
Format:	Article
Language:	en
Published:	King Saud bin Abdulaziz University 2024
Online Access:	http://psasir.upm.edu.my/id/eprint/118433/1/118433.pdf http://psasir.upm.edu.my/id/eprint/118433/ https://linkinghub.elsevier.com/retrieve/pii/S1319157824001010
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