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Convolutional neural networks (CNNs) have been used quite successfully forsemantic segmentation of brain tumors. However, current CNNs and attentionmechanisms are stochastic in nature and neglect the morphological indicatorsused by radiologists to manually annotate regions of interest. In this paper,we introduce a channel and spatial wise asymmetric attention (CASPIAN) byleveraging the inherent structure of tumors to detect regions of saliency. Todemonstrate the efficacy of our proposed layer, we integrate this into awell-established convolutional neural network (CNN) architecture to achievehigher Dice scores, with less GPU resources. Also, we investigate the inclusionof auxiliary multiscale and multiplanar attention branches to increase thespatial context crucial in semantic segmentation tasks. The resultingarchitecture is the new CASPIANET++, which achieves Dice Scores of 91.19% wholetumor, 87.6% for tumor core and 81.03% for enhancing tumor. Furthermore, drivenby the scarcity of brain tumor data, we investigate the Noisy Student methodfor segmentation tasks. Our new Noisy Student Curriculum Learning paradigm,which infuses noise incrementally to increase the complexity of the trainingimages exposed to the network, further boosts the enhancing tumor region to81.53%. Additional validation performed on the BraTS2020 data shows that theNoisy Student Curriculum Learning method works well without any additionaltraining or finetuning.

CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention  Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor  Segmentation