论文标题
使用BERT功能的两阶段COVID19分类
Two-Stage COVID19 Classification Using BERT Features
论文作者
论文摘要
我们建议使用双Bert特征提取从肺CT-SCAN切片图像中提出一个自动COVID1-19诊断框架。在第一个BERT特征提取中，首先使用3D-CNN提取CNN内部特征图。伯特·伯特（Bert）的时间po不使用全球平均池，而是用于在这些特征图中汇总时间信息，然后进行分类层。首先，该3D-CNN-BERT分类网络对每个原始CT扫描量的固定数量的图像进行了训练。在第二阶段，在每个CT扫描量的所有切片图像上都提取了3D-CNN-BERT嵌入功能，并且将这些特征平均为固定数量的片段。然后，另一个BERT网络用于将这些多个功能汇总到单个功能中，然后是另一个分类层。将两个阶段的分类结果组合在一起以生成最终输出。在验证数据集上，我们达到了0.9164的宏F1分数。
We propose an automatic COVID1-19 diagnosis framework from lung CT-scan slice images using double BERT feature extraction. In the first BERT feature extraction, A 3D-CNN is first used to extract CNN internal feature maps. Instead of using the global average pooling, a late BERT temporal pooing is used to aggregate the temporal information in these feature maps, followed by a classification layer. This 3D-CNN-BERT classification network is first trained on sampled fixed number of slice images from every original CT scan volume. In the second stage, the 3D-CNN-BERT embedding features are extracted on all slice images of every CT scan volume, and these features are averaged into a fixed number of segments. Then another BERT network is used to aggregate these multiple features into a single feature followed by another classification layer. The classification results of both stages are combined to generate final outputs. On the validation dataset, we achieve macro F1 score of 0.9164.
