Active learning with deep Bayesian neural network for laser control

2018 ◽  
Author(s):  
Sandrine I. Herriot ◽  
Brenda Ng ◽  
Wade H. Williams ◽  
Sachin S. Talathi ◽  
Thomas Spinka ◽  
...  
Keyword(s):  
Neural Network ◽  
Active Learning ◽  
Bayesian Neural Network ◽  
Laser Control

scholarly journals BNNpriors: A library for Bayesian neural network inference with different prior distributions

2021 ◽  
pp. 100079
Author(s):  
Vincent Fortuin ◽  
Adrià Garriga-Alonso ◽  
Mark van der Wilk ◽  
Laurence Aitchison
Keyword(s):  
Neural Network ◽  
Network Inference ◽  
Bayesian Neural Network ◽  
Prior Distributions

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.

COMPARISON OF SUPPORT VECTOR MACHINES AND BAYESIAN NEURAL NETWORKS PERFORMANCE FOR BREAST TISSUES USING GEOSTATISTICAL FUNCTIONS IN MAMMOGRAPHIC IMAGES

2010 ◽  
Vol 09 (04) ◽  
pp. 271-288
Author(s):  
GERALDO BRAZ JUNIOR ◽  
LEONARDO DE OLIVEIRA MARTINS ◽  
ARISTÓFANES CORREA SILVA ◽  
ANSELMO CARDOSO PAIVA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Region Of Interest ◽  
Female Breast Cancer ◽  
Support Vector ◽  
Risk Minimization ◽  
Bayesian Neural Network ◽  
Cad System ◽  
Mammographic Images ◽  
Breast Tissues

Female breast cancer is a major cause of deaths in occidental countries. Computer-aided Detection (CAD) systems can aid radiologists to increase diagnostic accuracy. In this work, we present a comparison between two classifiers applied to the separation of normal and abnormal breast tissues from mammograms. The purpose of the comparison is to select the best prediction technique to be part of a CAD system. Each region of interest is classified through a Support Vector Machine (SVM) and a Bayesian Neural Network (BNN) as normal or abnormal region. SVM is a machine-learning method, based on the principle of structural risk minimization, which shows good performance when applied to data outside the training set. A Bayesian Neural Network is a classifier that joins traditional neural networks theory and Bayesian inference. We use a set of measures obtained by the application of the semivariogram, semimadogram, covariogram, and correlogram functions to the characterization of breast tissue as normal or abnormal. The results show that SVM presents best performance for the classification of breast tissues in mammographic images. The tests indicate that SVM has more generalization power than the BNN classifier. BNN has a sensibility of 76.19% and a specificity of 79.31%, while SVM presents a sensibility of 74.07% and a specificity of 98.77%. The accuracy rate for tests is 78.70% and 92.59% for BNN and SVM, respectively.

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