  We propose a new residual block for convolutional neural networks and
demonstrate its state-of-the-art performance in medical image segmentation. We
combine attention mechanisms with group convolutions to create our group
attention mechanism, which forms the fundamental building block of our network,
FocusNet++. We employ a hybrid loss based on balanced cross entropy, Tversky
loss and the adaptive logarithmic loss to enhance the performance along with
fast convergence. Our results show that FocusNet++ achieves state-of-the-art
results across various benchmark metrics for the ISIC 2018 melanoma
segmentation and the cell nuclei segmentation datasets with fewer parameters
and FLOPs.
