Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans

Creating large scale high-quality annotations is a known challenge in medical imaging. In this work, based on the CycleGAN algorithm, we propose leveraging annotations from one modality to be useful in other modalities... (read more)

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Methods used in the Paper


METHOD TYPE
Concatenated Skip Connection
Skip Connections
Max Pooling
Pooling Operations
Batch Normalization
Normalization
Residual Connection
Skip Connections
U-Net
Semantic Segmentation Models
PatchGAN
Discriminators
ReLU
Activation Functions
Tanh Activation
Activation Functions
Residual Block
Skip Connection Blocks
Instance Normalization
Normalization
Convolution
Convolutions
Leaky ReLU
Activation Functions
Sigmoid Activation
Activation Functions
GAN Least Squares Loss
Loss Functions
Cycle Consistency Loss
Loss Functions
CycleGAN
Generative Models