Cross-dataset domain adaptation for the classification COVID-19 using
  chest computed tomography images
Ridha Ouni Haikel Alhichri
Detecting COVID-19 patients using Computed Tomography (CT) images of the
lungs is an active area of research. Datasets of CT images from COVID-19
patients are becoming available. Deep learning (DL) solutions and in particular
Convolutional Neural Networks (CNN) have achieved impressive results for the
classification of COVID-19 CT images, but only when the training and testing
take place within the same dataset. Work on the cross-dataset problem is still
limited and the achieved results are low. Our work tackles the cross-dataset
problem through a Domain Adaptation (DA) technique with deep learning. Our
proposed solution, COVID19-DANet, is based on pre-trained CNN backbone for
feature extraction. For this task, we select the pre-trained Efficientnet-B3
CNN because it has achieved impressive classification accuracy in previous
work. The backbone CNN is followed by a prototypical layer which is a concept
borrowed from prototypical networks in few-shot learning (FSL). It computes a
cosine distance between given samples and the class prototypes and then
converts them to class probabilities using the Softmax function. To train the
COVID19-DANet model, we propose a combined loss function that is composed of
the standard cross-entropy loss for class discrimination and another entropy
loss computed over the unlabelled target set only. This so-called unlabelled
target entropy loss is minimized and maximized in an alternative fashion, to
reach the two objectives of class discrimination and domain invariance.
COVID19-DANet is tested under four cross-dataset scenarios using the
SARS-CoV-2-CT and COVID19-CT datasets and has achieved encouraging results
compared to recent work in the literature.