no code implementations • 8 Sep 2020 • Qingsong Yao, Li Xiao, Peihang Liu, S. Kevin Zhou
Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via pixel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans.