Selected article for: "classification segmentation and Model training"
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_12
    Snippet: We proposed a combined "segmentation -classification" model pipeline, which highlighted the lesion regions in addition to the screening result. The model pipeline was divided into two stages: 3D segmentation and classification. The pipeline leveraged the model library we had previously developed. This library contained the state-of-the-art segmentation models such as fully convolutional network (FCN-8s) 13 , U-Net 14 , V-Net 15 , and 3D U-Net++ 1.....
    Document: We proposed a combined "segmentation -classification" model pipeline, which highlighted the lesion regions in addition to the screening result. The model pipeline was divided into two stages: 3D segmentation and classification. The pipeline leveraged the model library we had previously developed. This library contained the state-of-the-art segmentation models such as fully convolutional network (FCN-8s) 13 , U-Net 14 , V-Net 15 , and 3D U-Net++ 12 , as well as classification models like dual path network (DPN-92) 16 , Inception-v3 17 , residual network (ResNet-50) 11 , and Attention ResNet-50 18 . We selected the best diagnosis model by empirically training and evaluating the models within the library. The latest segmentation model was trained on 732 cases (704 contained inflammation or tumors). The 3D U-Net++ model obtained the highest Dice coefficient of 0.754, and Table S3 5 All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

    Search related documents:
    Co phrase search for related documents
    • art state and diagnosis model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
    • art state and dice coefficient: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
    • art state and fully convolutional network: 1, 2, 3, 4, 5, 6, 7, 8
    • art state and lesion region: 1, 2, 3
    • art state and library model: 1
    • art state and model library: 1
    • art state and model pipeline: 1, 2, 3, 4, 5
    • art state and path network: 1, 2, 3, 4
    • classification model and diagnosis model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22
    • classification model and dice coefficient: 1, 2, 3, 4, 5
    • classification model and fully convolutional network: 1
    • classification model and lesion region: 1, 2
    • classification model and library model: 1, 2
    • classification model and model library: 1, 2
    • classification model and model pipeline: 1
    • diagnosis model and dice coefficient: 1, 2
    • diagnosis model and good diagnosis model: 1, 2
    • dice coefficient and fully convolutional network: 1, 2, 3, 4
    • dice coefficient and high dice coefficient: 1