RGMIM: Region-Guided Masked Image Modeling for COVID-19 Detection
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Self-supervised learning has developed rapidly and also advances computer-aided diagnosis in the medical field. Masked image modeling (MIM) is one of the self-supervised learning methods that masks a portion of input pixels and tries to predict the masked pixels. Traditional MIM methods often use a random masking strategy. However, medical images often have a small region of interest for disease detection compared to ordinary images. For example, the regions outside the lung do not contain the information for decision, which may cause the random masking strategy not to learn enough information for COVID-19 detection. Hence, we propose a novel region-guided masked image modeling method (RGMIM) for COVID-19 detection in this paper. In our method, we design a new masking strategy that uses lung mask information to locate valid regions to learn more helpful information for COVID-19 detection. Experimental results show that RGMIM can outperform other state-of-the-art self-supervised learning methods on an open COVID-19 radiography dataset.
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