  Convolutional neural networks (CNNs) have been used quite successfully for
semantic segmentation of brain tumors. However, current CNNs and attention
mechanisms are stochastic in nature and neglect the morphological indicators
used by radiologists to manually annotate regions of interest. In this paper,
we introduce a channel and spatial wise asymmetric attention (CASPIAN) by
leveraging the inherent structure of tumors to detect regions of saliency. To
demonstrate the efficacy of our proposed layer, we integrate this into a
well-established convolutional neural network (CNN) architecture to achieve
higher Dice scores, with less GPU resources. Also, we investigate the inclusion
of auxiliary multiscale and multiplanar attention branches to increase the
spatial context crucial in semantic segmentation tasks. The resulting
architecture is the new CASPIANET++, which achieves Dice Scores of 91.19% whole
tumor, 87.6% for tumor core and 81.03% for enhancing tumor. Furthermore, driven
by the scarcity of brain tumor data, we investigate the Noisy Student method
for segmentation tasks. Our new Noisy Student Curriculum Learning paradigm,
which infuses noise incrementally to increase the complexity of the training
images exposed to the network, further boosts the enhancing tumor region to
81.53%. Additional validation performed on the BraTS2020 data shows that the
Noisy Student Curriculum Learning method works well without any additional
training or finetuning.
