Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset

This paper proposes a novel, unsupervised super-resolution (SR) approach for performing the SR of a clinical CT into the resolution level of a micro CT ($\mu$CT). The precise non-invasive diagnosis of lung cancer typically utilizes clinical CT data... (read more)

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Methods used in the Paper


METHOD TYPE
Max Pooling
Pooling Operations
Softmax
Output Functions
Dropout
Regularization
Sigmoid Activation
Activation Functions
Batch Normalization
Normalization
VGG
Convolutional Neural Networks
Cycle Consistency Loss
Loss Functions
Residual Connection
Skip Connections
SRGAN Residual Block
Skip Connection Blocks
PReLU
Activation Functions
Residual Block
Skip Connection Blocks
ReLU
Activation Functions
VGG Loss
Loss Functions
Instance Normalization
Normalization
PixelShuffle
Miscellaneous Components
Convolution
Convolutions
Leaky ReLU
Activation Functions
Dense Connections
Feedforward Networks
Tanh Activation
Activation Functions
PatchGAN
Discriminators
GAN Least Squares Loss
Loss Functions
CycleGAN
Generative Models
SRGAN
Generative Adversarial Networks