Ensembling and Test Augmentation for Covid-19 Detection and Covid-19
  Domain Adaptation from 3D CT-Scans
Fares Bougourzi Feryal Windal Moula Halim Benhabiles Fadi Dornaika Abdelmalik Taleb-Ahmed
Since the emergence of Covid-19 in late 2019, medical image analysis using
artificial intelligence (AI) has emerged as a crucial research area,
particularly with the utility of CT-scan imaging for disease diagnosis. This
paper contributes to the 4th COV19D competition, focusing on Covid-19 Detection
and Covid-19 Domain Adaptation Challenges. Our approach centers on lung
segmentation and Covid-19 infection segmentation employing the recent CNN-based
segmentation architecture PDAtt-Unet, which simultaneously segments lung
regions and infections. Departing from traditional methods, we concatenate the
input slice (grayscale) with segmented lung and infection, generating three
input channels akin to color channels. Additionally, we employ three 3D CNN
backbones Customized Hybrid-DeCoVNet, along with pretrained 3D-Resnet-18 and
3D-Resnet-50 models to train Covid-19 recognition for both challenges.
Furthermore, we explore ensemble approaches and testing augmentation to enhance
performance. Comparison with baseline results underscores the substantial
efficiency of our approach, with a significant margin in terms of F1-score (14
%). This study advances the field by presenting a comprehensive methodology for
accurate Covid-19 detection and adaptation, leveraging cutting-edge AI
techniques in medical image analysis.