  In our previous work, $i.e.$, HNF-Net, high-resolution feature representation
and light-weight non-local self-attention mechanism are exploited for brain
tumor segmentation using multi-modal MR imaging. In this paper, we extend our
HNF-Net to HNF-Netv2 by adding inter-scale and intra-scale semantic
discrimination enhancing blocks to further exploit global semantic
discrimination for the obtained high-resolution features. We trained and
evaluated our HNF-Netv2 on the multi-modal Brain Tumor Segmentation Challenge
(BraTS) 2021 dataset. The result on the test set shows that our HNF-Netv2
achieved the average Dice scores of 0.878514, 0.872985, and 0.924919, as well
as the Hausdorff distances ($95\%$) of 8.9184, 16.2530, and 4.4895 for the
enhancing tumor, tumor core, and whole tumor, respectively. Our method won the
RSNA 2021 Brain Tumor AI Challenge Prize (Segmentation Task), which ranks 8th
out of all 1250 submitted results.
