Selected article for: "ai system and diagnosis system"
Author: Xueyan Mei; Hao-Chih Lee; Kaiyue Diao; Mingqian Huang; Bin Lin; Chenyu Liu; Zongyu Xie; Yixuan Ma; Philip M. Robson; Michael Chung; Adam Bernheim; Venkatesh Mani; Claudia Calcagno; Kunwei Li; Shaolin Li; Hong Shan; Jian Lv; Tongtong Zhao; Junli Xia; Qihua Long; Sharon Steinberger; Adam Jacobi; Timothy Deyer; Marta Luksza; Fang Liu; Brent P. Little; Zahi A. Fayad; Yang Yang
Title: Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19)
  • Document date: 2020_4_17
    • ID: 79tozwzq_50
    Snippet: Similar to the previously reported AI diagnosis system 23, 24 , our algorithm consisted of two CNN subsystems to firstly identify the abnormal CT slices and then to perform region-specific disease diagnosis (Fig. 1 ). More specifically, the slice selection CNN was trained to evaluate a chest CT slice and assign a probability that it was normal. The inverse of this probability was then used to rank the abnormal slices of each CT scan. We selected .....
    Document: Similar to the previously reported AI diagnosis system 23, 24 , our algorithm consisted of two CNN subsystems to firstly identify the abnormal CT slices and then to perform region-specific disease diagnosis (Fig. 1 ). More specifically, the slice selection CNN was trained to evaluate a chest CT slice and assign a probability that it was normal. The inverse of this probability was then used to rank the abnormal slices of each CT scan. We selected the 10 most abnormal slices from each study for the subsequent disease diagnosis due to the tradeoff of efficiency and turn-around time.

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