Selected article for: "ratio set and validation 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.

    Search related documents:
    Co phrase search for related documents
    • CT image and training set: 1, 2, 3, 4, 5, 6, 7
    • CT image and validation set: 1
    • dataset split and training set: 1, 2, 3, 4
    • dataset split and validation set: 1, 2
    • non ct and training set: 1, 2, 3, 4
    • non ct and validation set: 1, 2, 3, 4
    • set statistic and training set: 1, 2
    • set statistic and validation set: 1, 2, 3