RBA-Net: Binary Cross-Entropy Loss for Improved Segmentation Accuracy

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