SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation
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The global pandemic of COVID-19 has infected millions of people since its first outbreak in last December. A key challenge for preventing and controlling COVID-19 is how to quickly, widely, and effectively implement the test for the disease, because testing is the first step to break the chains of transmission. To assist the diagnosis of the disease, radiology imaging is used to complement the screening process and triage patients into different risk levels. Deep learning methods have taken a more active role in automatically detecting COVID-19 disease in chest x-ray images, as witnessed in many recent works. Most of these works first train a CNN on an existing large-scale chest x-ray image dataset and then fine-tune it with a COVID-19 dataset at a much smaller scale. However, direct transfer across datasets from different domains may lead to poor performance due to visual domain shift. Also, the small scale of the COVID-19 dataset on the target domain can make the training fall into the overfitting trap. To solve all these crucial problems and fully exploit the available large-scale chest x-ray image dataset, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting, through which we are motivated to reduce the domain shift and avoid overfitting when training on a very small dataset of COVID-19. In addressing this formulated problem, we propose a novel Semi-supervised Open set Domain Adversarial network (SODA), which is able to align the data distributions across different domains in a general domain space and also in a common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models, as well as effectively separating COVID-19 with common pneumonia.
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