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.

这篇论文概述了我们在 “计算机视觉与模式识别会议（CVPR）” 的 “医学图像分析中的领域适应、可解释性和公平性 AI（DEF-AI-MIA）” 研讨会上，参加第四届 COV19D 竞赛的提交内容。竞赛包括两个挑战，第一个是从 COV19-CT-DB 数据库的一千多个 CT 扫描中训练一个分类器来检测 COVID-19 的存在。第二个挑战是通过获取来自挑战 1 的数据集并添加一小部分扫描（有些注释有些没有）来执行领域适应。我们对 CT 扫描进行了预处理以分割肺部，并输出了包含单独和整体肺部的体积。然后我们在这些输入上训练了 3D ResNet 和 Swin Transformer 模型。我们使用这些模型的整合对未标记的 CT 扫描进行了注释，并选择高置信度的预测结果作为微调的伪标签。结果竞赛 1 的最佳交叉验证 F1 得分为 93.39％，竞赛 2 的平均 F1 得分为 92.15。