Ensembling and Test Augmentation for Covid-19 Detection and Covid-19 Domain Adaptation from 3D CT-Scans

arXiv:2403.11338v1 Announce Type: cross
Abstract: 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 …

abstract analysis artificial artificial intelligence arxiv augmentation competition covid covid-19 cs.cv cs.lg detection diagnosis disease disease diagnosis domain domain adaptation eess.iv emergence image imaging intelligence medical paper research scans test type utility