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. Author manuscript; available in PMC: 2020 Apr 1.
Published in final edited form as: IEEE Trans Med Imaging. 2018 Oct 12;38(4):919–931. doi: 10.1109/TMI.2018.2875814

TABLE II:

The network architecture parameters of the Tiramisu segmentation engine

Hyper-Parameters Value

Learning-Rate 0.00005
Drop-out 0.2
Network Weight Initialization Xavier Initializer
Bias Initializer Zero Initializer
Activation Function ReLu
Growth Rate 24
Normalization Batch Normalization

Network Parameters Value

Pooling Average
Batch-Size 3
Optimization Adam