Author: Shuo Jin; Bo Wang; Haibo Xu; Chuan Luo; Lai Wei; Wei Zhao; Xuexue Hou; Wenshuo Ma; Zhengqing Xu; Zhuozhao Zheng; Wenbo Sun; Lan Lan; Wei Zhang; Xiangdong Mu; Chenxi Shi; Zhongxiao Wang; Jihae Lee; Zijian Jin; Minggui Lin; Hongbo Jin; Liang Zhang; Jun Guo; Benqi Zhao; Zhizhong Ren; Shuhao Wang; Zheng You; Jiahong Dong; Xinghuan Wang; Jianming Wang; Wei Xu
Title: AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks Document date: 2020_3_23
ID: e6q92shw_10
Snippet: As illustrated in Figure 1 , we designed our method to solve the above challenges: 1) Taking advantage of the end-to-end deep neural network models, we were able to adapt the models we had developed previously for other diagnoses, such as ResNet-50 11 and 3D Unet++ 12 , and construct a training-inference pipeline, evaluating a variety of candidate models quickly. 2) For the training data, in addition to the positive cases, we carefully assembled .....
Document: As illustrated in Figure 1 , we designed our method to solve the above challenges: 1) Taking advantage of the end-to-end deep neural network models, we were able to adapt the models we had developed previously for other diagnoses, such as ResNet-50 11 and 3D Unet++ 12 , and construct a training-inference pipeline, evaluating a variety of candidate models quickly. 2) For the training data, in addition to the positive cases, we carefully assembled a set of negative images of inflammatory and neoplastic pulmonary diseases, such as lobar pneumonia, lobster pneumonia, and old lesions. Thus the model could learn the different features of COVID-19 from others. We also got 4 All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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