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. Author manuscript; available in PMC: 2019 Nov 22.
Published in final edited form as: Proc SPIE Int Soc Opt Eng. 2019 Mar 15;10949:1094908. doi: 10.1117/12.2512842

Table 1.

The network details of each part of SSNet used in the experiment.

Convolutional Layer Input Channel=3, Output Channel=64, Stride=2. Padding=3
BatchNorm2D
ReLU()
MaxPooling(Stride=2, padding=1, dilation=1)
ResNet50 Block1 Bottleneck (64, 256)
ResNet50 Block2 Bottleneck (256, 512)
ResNet50 Block3 Bottleneck (512, 1024)
ResNet50 Block4 Bottleneck (1024, 2048)
Boundary Refinement Layer BatchNorm2D ()
ReLU ()
Conv2d (Input Channel=2, Output Channel=2, Stride=1, Padding=1)
Up-Sampling Bilinear Decoder
GCN Connector Conv2d (Kernel Size=7, Stride=1, Padding=3)