• R. S. Randhawa, A. Modi, P. Jain, and P. Warier, Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. deep learning cnn ai healthcare
    • 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.
  • R. S. Randhawa, P. Jain, and G. Madan, Topic Modeling Using Distributed Word Embeddings. Feb 2016. Try Vec2Topic deep learning machine vec2topic
    • 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.