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

• Main Authors: Zhuoyi, Tan, Hizmawati, Madzin, Bahari, Norafida, Rahmita, Wirza OK Rahmat, Fatimah, Khalid, Puteri, Suhaiza Sulaiman
• Format: Article
• Language: English
• Published: Elsevier 2024
• Online Access: http://psasir.upm.edu.my/id/eprint/111381/1/SwinUNeLCsT.pdf; http://psasir.upm.edu.my/id/eprint/111381/; https://www.sciencedirect.com/science/article/pii/S1319157824001010

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,...