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- W2728804654 abstract "The deep neural network (DNN) that models characteristics of general blood test (GBT) results was used in clinical opinions generation. The DNN that generates clinical opinions has the complex structure, which causes overfitting problem. The relatively small size of medical dataset also contributes to the occurrence of overfitting. In order to deal with overfitting, we apply two techniques that solve overfitting of DNN, which are dropout, and batch normalization. Dropout is inserted into the network in various ways in order to find out the optimal structure of the network. Batch normalization is also added in various ways for the same purpose. The experiment conducted on GBT dataset shows that DNNs with dropout and batch normalization outperform the simple DNN in generating clinical opinions for our GBT dataset. Besides, dropout shows slightly better performance compared to batch normalization." @default.
- W2728804654 created "2017-07-14" @default.
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- W2728804654 date "2017-05-01" @default.
- W2728804654 modified "2023-09-25" @default.
- W2728804654 title "Model Regularization of Deep Neural Networks for Robust Clinical Opinions Generation from General Blood Test Results" @default.
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- W2728804654 doi "https://doi.org/10.1109/mdm.2017.67" @default.
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