  We utilize 3-D fully convolutional neural networks (CNN) to segment gliomas
and its constituents from multimodal Magnetic Resonance Images (MRI). The
architecture uses dense connectivity patterns to reduce the number of weights
and residual connections and is initialized with weights obtained from training
this model with BraTS 2018 dataset. Hard mining is done during training to
train for the difficult cases of segmentation tasks by increasing the dice
similarity coefficient (DSC) threshold to choose the hard cases as epoch
increases. On the BraTS2020 validation data (n = 125), this architecture
achieved a tumor core, whole tumor, and active tumor dice of 0.744, 0.876,
0.714,respectively. On the test dataset, we get an increment in DSC of tumor
core and active tumor by approximately 7%. In terms of DSC, our network
performances on the BraTS 2020 test data are 0.775, 0.815, and 0.85 for
enhancing tumor, tumor core, and whole tumor, respectively. Overall survival of
a subject is determined using conventional machine learning from rediomics
features obtained using a generated segmentation mask. Our approach has
achieved 0.448 and 0.452 as the accuracy on the validation and test dataset.
