High-confidence pseudo-labels for domain adaptation in COVID-19 detection

arXiv:2403.13509v1 Announce Type: cross
Abstract: This paper outlines our submission for the 4th COV19D competition as part of the `Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis' (DEF-AI-MIA) workshop at the Computer Vision and Pattern Recognition Conference (CVPR). The competition consists of two challenges. The first is to train a classifier to detect the presence of COVID-19 from over one thousand CT scans from the COV19-CT-DB database. The second challenge is to perform domain adaptation by taking the …

abstract analysis arxiv challenges competition computer computer vision conference confidence covid covid-19 cs.cv cvpr detection domain domain adaptation eess.iv explainability fairness fairness in ai image labels medical outlines paper part pattern recognition recognition train type vision workshop