Enhanced LSTM with Batch Normalization

2019 ◽  
pp. 746-755 ◽  
Author(s):  
Li-Na Wang ◽  
Guoqiang Zhong ◽  
Shoujun Yan ◽  
Junyu Dong ◽  
Kaizhu Huang
Keyword(s):  
Batch Normalization

A novel solution of using deep learning for prostate cancer segmentation: enhanced batch normalization

2021 ◽  
Author(s):  
Sushma Shrestha ◽  
Abeer Alsadoon ◽  
P. W. C. Prasad ◽  
Indra Seher ◽  
Omar Hisham Alsadoon
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Batch Normalization

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

Revisiting Internal Covariate Shift for Batch Normalization

2020 ◽  
pp. 1-11
Author(s):  
Muhammad Awais ◽  
Md. Tauhid Bin Iqbal ◽  
Sung-Ho Bae
Keyword(s):  
Batch Normalization

Representative Batch Normalization with Feature Calibration

2021 ◽  
Author(s):  
Shang-Hua Gao ◽  
Qi Han ◽  
Duo Li ◽  
Ming-Ming Cheng ◽  
Pai Peng
Keyword(s):  
Batch Normalization
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