  As intensities of MRI volumes are inconsistent across institutes, it is
essential to extract universal features of multi-modal MRIs to precisely
segment brain tumors. In this concept, we propose a volumetric vision
transformer that follows two windowing strategies in attention for extracting
fine features and local distributional smoothness (LDS) during model training
inspired by virtual adversarial training (VAT) to make the model robust. We
trained and evaluated network architecture on the FeTS Challenge 2022 dataset.
Our performance on the online validation dataset is as follows: Dice Similarity
Score of 81.71%, 91.38% and 85.40%; Hausdorff Distance (95%) of 14.81 mm, 3.93
mm, 11.18 mm for the enhancing tumor, whole tumor, and tumor core,
respectively. Overall, the experimental results verify our method's
effectiveness by yielding better performance in segmentation accuracy for each
tumor sub-region. Our code implementation is publicly available :
https://github.com/himashi92/vizviva_fets_2022
