High-confidence pseudo-labels for domain adaptation in COVID-19
  detection
Robert Turnbull Simon Mutch
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 dataset from Challenge 1 and adding a small number of
scans (some annotated and other not) for a different distribution. We
preprocessed the CT scans to segment the lungs, and output volumes with the
lungs individually and together. We then trained 3D ResNet and Swin Transformer
models on these inputs. We annotated the unlabeled CT scans using an ensemble
of these models and chose the high-confidence predictions as pseudo-labels for
fine-tuning. This resulted in a best cross-validation mean F1 score of 93.39\%
for Challenge 1 and a mean F1 score of 92.15 for Challenge 2.