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A schematic illustration of the proposed bidirectional attention recurrent network (BARNet) for ED-frame detection. The network consists of two bidirectional RNN layers (GRU or LSTM), followed by an attention layer to learn long-term dependencies, and finally generating the predicted ED-frame likelihoods. The encoded IVUS features are input into the network, and the output is the probability of each frame being an ED-frame.
