Tracking the progression of brain tumors is a challenging
task, due to the slow growth rate and the combination of different tumor
components, such as cysts, enhancing patterns, edema and necrosis. In
this paper, we propose a Deep Neural Network based architecture that
does automatic segmentation of brain tumor, and focuses on improving
accuracy at the edges of these different classes. We show that enhancing
the loss function to give more weight to the edge pixels significantly
improves the neural network’s accuracy at classifying the boundaries. In
the BRATS 2016 challenge, our submission placed third on the task of
predicting progression for the complete tumor region.
We propose a new algorithm for topic modeling, Vec2Topic, that identifies the main topics in a corpus using semantic information captured via high-dimensional distributed word embeddings. Our technique is unsupervised and generates a list of topics ranked with respect to importance. We find that it works better than existing topic modeling techniques such as Latent Dirichlet Allocation for identifying key topics in user-generated content, such as emails, chats, etc., where topics are diffused across the corpus. We also find that Vec2Topic works equally well for non-user generated content, such as papers, reports, etc., and for small corpora such as a single-document.