JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

• Published in: IEEE transactions on image processing Vol. 30; pp. 3113 - 3126
• Main Authors: Wu, Yu-Huan, Gao, Shang-Hua, Mei, Jie, Xu, Jun, Fan, Deng-Ping, Zhang, Rong-Guo, Cheng, Ming-Ming
• Format: Journal Article
• Language: English
• Published: United States IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Attenuation; Chest; Classification; Computed tomography; Coronaviruses; COVID-19; COVID-19 - diagnostic imaging; COVID-19 dataset; CT classification; CT segmentation; Databases, Factual; Datasets; Deep Learning; Diagnosis; Female; Humans; Image classification; Image segmentation; joint diagnosis; Lung; Lung - diagnostic imaging; Male; Medical imaging; Middle Aged; Pandemics; Polymerase chain reaction; Radiographic Image Interpretation, Computer-Assisted - methods; SARS-CoV-2; Sensitivity; Test sets; Tomography, X-Ray Computed; Viral diseases; X-rays; Young Adult

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase C...