Multi-attention representation network partial domain adaptation for COVID-19 diagnosis

The outbreak of COVID-19 threatens the safety of all human beings. Rapid and accurate diagnosis of patients is the effective way to prevent the rapid spread of COVID-19. The current computer-aided diagnosis of COVID-19 requires extensive labeled data for training, and this undoubtedly increases human and material resources costs. Domain adaptation (DA), an existing promising approach, can transfer knowledge from rich labeled pneumonia datasets for COVID-19 diagnosis and classification. However, due to the differences in feature distribution and task semantic between pneumonia and COVID-19, negative transfer may reduce the performance in diagnosis COVID-19 and pneumonia. Furthermore, the training data is usually mixed with many noise samples in practice, and this also poses new challenges for domain adaptation. As a kind of domain adaptation, partial domain adaptation (PDA) can well avoid outlier samples in the source domain and achieve good classification performance in the target domain. However, the existing PDA methods all learn a single feature representation; this can only learn local information about the inputs and ignore other important information in the samples. Therefore multi-attention representation network partial domain adaptation (MARPDA) is proposed in this paper to overcome the above shortcomings of PDA. In MARPDA, we construct the multiple representation networks with attention to acquire the image representation and effectively learn knowledge from different feature spaces. We design the sample-weighted strategy to achieve partial data transfer and address the negative transfer of noise data during training. MARPDA adapts to complex application scenarios and learns fine-grained features of the image from multiple representations. We apply the model to classify pneumonia and COVID-19 respectively, and evaluate it in qualitative and quantitative manners. The experimental results show that our classification accuracy is higher than that of the existing state-of-the-art methods. The stability and reliability of the proposed method are validated by the confusion matrix and the performance curves experiments. In summary, our method has better performance for diagnosis COVID-19 compared to the existing state-of-the-art methods.