Comparison of Patch-Based Conditional Generative Adversarial Neural Net Models with Emphasis on Model Robustness for Use in Head and Neck Cases for MR-Only planning

A total of twenty paired CT and MR images were used in this study to investigate two conditional generative adversarial networks, Pix2Pix, and Cycle GAN, for generating synthetic CT images for Headand Neck cancer cases. Ten of the patient cases were used for training and included such common artifacts as dental implants; the remaining ten testing cases were used for testing and included a larger range of image features commonly found in clinical head and neck cases... (read more)

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METHOD TYPE
Residual Connection
Skip Connections
GAN Least Squares Loss
Loss Functions
Cycle Consistency Loss
Loss Functions
Tanh Activation
Activation Functions
Residual Block
Skip Connection Blocks
Instance Normalization
Normalization
CycleGAN
Generative Models
Concatenated Skip Connection
Skip Connections
PatchGAN
Discriminators
ReLU
Activation Functions
Batch Normalization
Normalization
Leaky ReLU
Activation Functions
Sigmoid Activation
Activation Functions
Dropout
Regularization
Pix2Pix
Generative Models
Convolution
Convolutions
GAN
Generative Models