Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods

Keyphrases

• Supervised Learning Method 100%
• Signal Detection Task 100%
• Ideal Observer 100%
• Hotelling Observer 100%
• Binary Signals 100%
• Test Statistic 85%
• Object Model 42%
• Image Quality 28%
• Task-oriented 28%
• Medical Imaging Systems 28%
• Tumor 14%
• Receiver Operating Characteristic Analysis 14%
• Specific Task 14%
• Observer Performance 14%
• System Performance 14%
• Computational Challenges 14%
• Numerical Simulation 14%
• Monte Carlo Method 14%
• Markov Chain Monte Carlo 14%
• Numerical Methods 14%
• Learning-based 14%
• Detection Performance 14%
• Supervised Learning 14%
• Convolutional Neural Network 14%
• Bayesian Ideal Observer 14%
• Background Modeling 14%
• Analytical Calculation 14%
• Single-layer Neural Network 14%
• Large Covariance Matrix 14%
• Acquisition Design 14%
• Laplacian 14%
• Simple Objects 14%
• Mixed Poisson 14%
• Medical Image Dataset 14%
• Measurement Noise Model 14%

Computer Science

• Supervised Learning 100%
• Signal Detection 100%
• Image Quality 50%
• Medical Imaging 50%
• Systems Performance 25%
• markov chain monte-carlo 25%
• Measurement Noise 25%
• Covariance Matrix 25%
• Learning Approach 25%
• Convolutional Neural Network 25%
• Detection Performance 25%
• Laplace Operator 25%
• Numerical Simulation 25%
• Computational Simulation 25%
• Mathematical Method 25%
• Considered Model 25%
• Background Model 25%
• Layer Neural Network 25%
• System Acquisition 25%

Physics

• Signal Detection 100%
• Supervised Learning 100%
• Gaussian Distribution 50%
• Noise Measurement 25%
• Data Acquisition 25%
• Mathematical Method 25%
• Neural Network 25%
• Convolutional Neural Network 25%
• Covariance 25%
• Markov Chain Monte Carlo 25%
• Neoplasm 25%
• Tumor 25%
• Numerical Simulation 25%