Fig. 2
From: Self-supervised pre-training for joint optic disc and cup segmentation via attention-aware network
The overall architecture of the network. The given input image I is first fed into the encoder, yielding the multi-scale feature maps F. We employ the proposed multi-scale attention module followed by each convolutional layer for feature enhancement. Then, we inject the designed aggregation attention module followed by the last layer for feature fusion. The decoder is bridged behind the encoder in the pyramid-like structure for final mask prediction