deep learning

Magnetic resonance imaging (MRI) is being increasingly utilized to assess, diagnose, and plan treatment for a variety of diseases. The ability to visualize tissue in varied contrasts in the form of MR pulse sequences in a single scan provides …

Single molecule localization microscopy (SMLM) allows unprecedented insight into the three-dimensional organization of proteins at the nanometer scale. The combination of minimal invasive cell imaging with high resolution positions SMLM at the …

Invited to present my research in which we proposed a multi-input, multi-output generative adversarial network (GAN) called MM-GAN as a poster presentation.

Invited to present my research in which we proposed a multi-input, multi-output generative adversarial network (GAN) called MM-GAN.

Presented an interesting paper from ICLR 2019 that performs out-of-distribution sample detection using a simple framework.

Presenting a highly scientific paper in a way that high school students can understand. The paper is a state-of-art method that uses natural language processing based approach towards structural modelling of proteins.

Literature survey on the uses of text mining and natural language processing methods in bioinformatics.

A talk regarding the current state-of-art in handling missing inputs for segmentation or classification problems.

Inspired by the success of Convolutional Neural Networks (CNN), we develop a novel Computer Aided Detection (CADe) system using CNN for Glioblastoma Multiforme (GBM) detection and segmentation from multi channel MRI data. A two-stage approach first …

Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients. They equip encoder-decoder-like networks …