Author: Amine Amyar; Romain Modzelewski; Su Ruan
Title: Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation Document date: 2020_4_21
ID: hiac6ur7_40
Snippet: is the (which was not peer-reviewed) The copyright holder for this preprint . lung cancer, pneumonia or normal cases were selected randomly in train, validation and test. For a fair comparison, the other methods were trained, validated and tested in the same group of data. The performance of the models were evaluated using the dice coefficient for the segmentation task, and the accuracy (Acc), sensitivity (Sens), specificity (Spec) and area under.....
Document: is the (which was not peer-reviewed) The copyright holder for this preprint . lung cancer, pneumonia or normal cases were selected randomly in train, validation and test. For a fair comparison, the other methods were trained, validated and tested in the same group of data. The performance of the models were evaluated using the dice coefficient for the segmentation task, and the accuracy (Acc), sensitivity (Sens), specificity (Spec) and area under the ROC curve (AUC) for the classification.
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