  We present an automatic COVID1-19 diagnosis framework from lung CT-scan slice
images. In this framework, the slice images of a CT-scan volume are first
proprocessed using segmentation techniques to filter out images of closed lung,
and to remove the useless background. Then a resampling method is used to
select one or multiple sets of a fixed number of slice images for training and
validation. A 3D CNN network with BERT is used to classify this set of selected
slice images. In this network, an embedding feature is also extracted. In cases
where there are more than one set of slice images in a volume, the features of
all sets are extracted and pooled into a global feature vector for the whole
CT-scan volume. A simple multiple-layer perceptron (MLP) network is used to
further classify the aggregated feature vector. The models are trained and
evaluated on the provided training and validation datasets. On the validation
dataset, the accuracy is 0.9278 and the F1 score is 0.9261.
