RBA-Net: A Novel Architecture with Multi-Task Learning and Binary Cross-Entropy Loss

In our proposed RBA-Net, we utilize the binary cross-entropy loss to minimize the discrepancy between the prediction output from the decoder network and the ground-truth. The training process involves two components: the decoder loss and the joint loss of the multi-task learning module. The total loss function is defined as follows: