Introduction
PAI Purpose
PAI Overview
Architectures included in Distribution
uNET
Support Vector Machine (SVM)
Generative Adversarial Network (GAN)
ResNet50
Image Classification
Installation of PAI Infrastructure
Windows
Python and TensorFlow Installation
R Installation
Default R Configuration
Minimal R Configuration
Selection of Python installation
MacOS
Python and TensorFlow Installation
R Installation
Default R Configuration
Minimal R Configuration
Selection of Python installation
Linux Platforms
R Installation
Python and TensorFlow Installation
Preparation of Training Data and Neural Network
Data Preparation
Control Mechanism
Learning Sets
Training of Neural Network
Exporting an R workspace
Output Visualization for SVM Classification
Use of Trained Neural Network for Prediction
In the View tool
In Pipeline processing
In the PSEG tool
In the PNEURO tool
In the PCARDM tool
Data generation
Case Studies - Application of PAI
Rat Brain Dopaminergic PET
Sample Preparation
Training and Validation
Brain Tumor Segmentation - MICCAI Challenge
Sample Preparation
Training and Validation
Mouse Bone Trabecular Segmentation
Sample Preparation
Training and Validation
Mouse & Human Cardiac MR Cine Left Ventricle Segmentation
Sample Preparation
Training and Validation
Human Deep Nuclei Segmentation
Sample Preparation
Training and Validation
Human Amyloid PET Classification
Sample Preparation
Training and Validation
Appendix
Exporting an R Workspace and Training in a Cloud Computing Environment
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