Triple attention learning for classification of 14 thoracic diseases using chest radiography

• Published in: Medical image analysis Vol. 67; p. 101846
• Main Authors: Wang, Hongyu, Wang, Shanshan, Qin, Zibo, Zhang, Yanning, Li, Ruijiang, Xia, Yong
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2021; Elsevier BV
• Subjects: Abnormalities; Annotations; Attention mechanism; Chest; Chest radiography; Classification; Computer networks; Deep learning; Diagnosis; Diseases; Feature extraction; Feature maps; Machine learning; Radiography; Radiology; Thoracic disease classification; Thorax; Deep learning; Thoracic disease classification; Attention mechanism; Chest radiography

•Propose triple attention n etwork (A 3 Net) for thoracic disease diagnosis on chest X ray.•Learn channel wise, element wise, and scale wise attention s imultaneously.•Incorporate three attention learning mechanisms in to a deep classification model.•A chiev e highest average AUC on the ChestX ray14...