Selected article for: "ratio set and validation training set"
Author: Xuehai He; Xingyi Yang; Shanghang Zhang; Jinyu Zhao; Yichen Zhang; Eric Xing; Pengtao Xie
Title: Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans
  • Document date: 2020_4_17
    • ID: l3f469ht_48
    Snippet: Our collected COVID19-CT dataset consists of 349 COVID-19 CTs and 397 Non-COVID-19 CTs. The CT images were resized to 224 × 224. We split the dataset into a training set, a validation set, and a test set by patient IDs with a ratio of 0.6: 0.15: 0.25. Table I shows the statistics of the three sets......
    Document: Our collected COVID19-CT dataset consists of 349 COVID-19 CTs and 397 Non-COVID-19 CTs. The CT images were resized to 224 × 224. We split the dataset into a training set, a validation set, and a test set by patient IDs with a ratio of 0.6: 0.15: 0.25. Table I shows the statistics of the three sets.

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