Fig. 2: Diagrams of HRNet_FCN.
From: Deep learning HRNet FCN for blood vessel identification in laparoscopic pancreatic surgery
a Diagram of High-Resolu48tion Network (HRNet)-Full Convolutional Network (FCN). Images were scaled to 960 × 540 to input into HRNet_V2 Network for feature extraction. Feature maps at 1/4, 1/8, 1/16, and 1/32 levels were generated by HRNet_V2. After deconvolution, these feature maps were concatenated to form a fused feature map containing all information at different levels. Finally, the FCN performed convolution and deconvolution to obtain the result. b Details of the parallel structure. The original image underwent three phases to form feature maps at four different scaling levels. Parallel arrows represented convolution, downward arrows represented downsampling, and upward arrows represented upsampling.
