An Unsupervised Deep Learning Framework for Retrospective Gating of Catheter-Based Cardiac Imaging

Figure 3

Architecture of the UFL module. It consists of a 3 × 3 convolutional layer with 64 feature channels, four ResBlocks, an average pooling layer, and two FC layers. It is used to extract feature vectors from intravascular images frame by frame.