Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19

•A novel Contrastive Multi-Task Convolutional Neural Network (CMT-CNN) is proposed for automatic COVID-19 diagnosis.•The main task is to diagnose COVID-19 from other pneumonia and normal controls. The auxiliary task is self-supervised contrastive learning to acquire transformation-invariant representations.•A series of interpretable transformations are defined for medical image augmentation.•Extensive experiments demonstrate that the auxiliary task can significantly improve the generalization of CNN on both CT and X-ray datasets. Computed tomography (CT) and X-ray are effective methods for diagnosing COVID-19. Although several studies have demonstrated the potential of deep learning in the automatic diagnosis of COVID-19 using CT and X-ray, the generalization on unseen samples needs to be improved. To tackle this problem, we present the contrastive multi-task convolutional neural network (CMT-CNN), which is composed of two tasks. The main task is to diagnose COVID-19 from other pneumonia and normal control. The auxiliary task is to encourage local aggregation though a contrastive loss: first, each image is transformed by a series of augmentations (Poisson noise, rotation, etc.). Then, the model is optimized to embed representations of a same image similar while different images dissimilar in a latent space. In this way, CMT-CNN is capable of making transformation-invariant predictions and the spread-out properties of data are preserved. We demonstrate that the apparently simple auxiliary task provides powerful supervisions to enhance generalization. We conduct experiments on a CT dataset (4,758 samples) and an X-ray dataset (5,821 samples) assembled by open datasets and data collected in our hospital. Experimental results demonstrate that contrastive learning (as plugin module) brings solid accuracy improvement for deep learning models on both CT (5.49%-6.45%) and X-ray (0.96%-2.42%) without requiring additional annotations. Our codes are accessible online.