Abstract
The outbreak of the COVID-19 global pandemic has affected millions and has a severe impact on our daily lives. To support radiologists in this overwhelming challenge, we developed a weakly supervised deep learning framework that can detect, localize, and quantify the severity of COVID-19 disease from chest CT scans using limited annotations. The framework is designed to rapidly provide a solution during the initial outbreak of a pandemic when datasets availability is limited. It is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification, and fine-grained localization. In addition, we present the Coronascore bio-marker which corresponds to the severity grade of the disease. Finally, we present an unsupervised feature space clustering which can assist in understanding the COVID-19 radiographic manifestations. We present our results on an external dataset comprised of 199 patients from Zhejiang province, China.
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Gozes, O. et al. (2020). A Weakly Supervised Deep Learning Framework for COVID-19 CT Detection and Analysis. In: Petersen, J., et al. Thoracic Image Analysis. TIA 2020. Lecture Notes in Computer Science(), vol 12502. Springer, Cham. https://doi.org/10.1007/978-3-030-62469-9_8
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DOI: https://doi.org/10.1007/978-3-030-62469-9_8
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