Kelei He

TL;DR: This review paper covers the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up, and particularly focuses on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals.

TL;DR: In this article, the authors reviewed the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.

TL;DR: Experimental results show that the performance of the proposed method outperforms the baseline fully convolutional networks, as well as other state‐of‐the‐art methods in CT male pelvic organ segmentation.

TL;DR: A two-stage deep learning-based method, with a novel distinctive curve-guided fully convolutional network (FCN) designed as a coarse segmentation network to provide region proposals for three pelvic organs to solve the aforementioned challenges.

TL;DR: Wang et al. as discussed by the authors proposed a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification.