JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation
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Recently, the novel coronavirus 2019 (COVID-19) has caused a pandemic disease over 200 countries, influencing billions of humans. To control the infection, the first and key step is to identify and separate the infected people. But due to the lack of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, it is essential to discover suspected COVID-19 patients via CT scan analysis by radiologists. However, CT scan analysis is usually time-consuming, requiring at least 15 minutes per case. In this paper, we develop a novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID-19 diagnosis. To train our JCS system, we construct a large scale COVID-19 Classification and Segmentation (COVID-CS) dataset, with 144,167 CT images of 400 COVID-19 patients and 350 uninfected cases. 3,855 CT images of 200 patients are annotated with fine-grained pixel-level labels, lesion counts, infected areas and locations, benefiting various diagnosis aspects. Extensive experiments demonstrate that, the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0 and 78.3 online demo of our JCS diagnosis system will be available soon.
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