Selected article for: "feature extraction and international license"
Author: Cheng Jin; Weixiang Chen; Yukun Cao; Zhanwei Xu; Xin Zhang; Lei Deng; Chuansheng Zheng; Jie Zhou; Heshui Shi; Jianjiang Feng
Title: Development and Evaluation of an AI System for COVID-19
  • Document date: 2020_3_23
    • ID: k1lg8c7q_30
    Snippet: In order to further verify our conjecture, we performed radiomics [20] feature extraction on these attentional regions, and obtained a total of 752-dimensional imaging features. The Least Absolute Shrinkage and Selection Algorithm (LAS-SO) were used to find the most discriminative features ( Figure 6 ). A total of 15 features were finally chosen whose absolute values of coefficients are higher than 10-3. The cluster-heatmap and heat map of correl.....
    Document: In order to further verify our conjecture, we performed radiomics [20] feature extraction on these attentional regions, and obtained a total of 752-dimensional imaging features. The Least Absolute Shrinkage and Selection Algorithm (LAS-SO) were used to find the most discriminative features ( Figure 6 ). A total of 15 features were finally chosen whose absolute values of coefficients are higher than 10-3. The cluster-heatmap and heat map of correlation coefficient matrix before and after LASSO show that LASSO filtered out redundant and unimportant 10 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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