COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment
作者：
Highlights：
• Automated and simultaneous detection and severity assessment of COVID-19 using CT.
• Tackle under segmentation of ground-glass opacities in COVID-19 chest CT scans.
• 3D CNNs boosted by shifting low-level feature representations of volumetric inputs.
• COVID-19 multitask learning performance improved by random-weighted loss function.
• Identified features significantly related to positivity and severity of COVID-19.
摘要
•Automated and simultaneous detection and severity assessment of COVID-19 using CT.•Tackle under segmentation of ground-glass opacities in COVID-19 chest CT scans.•3D CNNs boosted by shifting low-level feature representations of volumetric inputs.•COVID-19 multitask learning performance improved by random-weighted loss function.•Identified features significantly related to positivity and severity of COVID-19.
论文关键词：COVID-19,Multitask learning,3D CNNs,Diagnosis,Severity assessment,Deep learning,Computer tomography
论文评审过程：Received 31 December 2020, Revised 11 November 2021, Accepted 10 December 2021, Available online 12 December 2021, Version of Record 20 December 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108499
