858 citations · 885 across the 11 of their papers we have counts for
14 papers
KDPhys: An Attention Guided 3D to 2D Knowledge Distillation for Real-time Video-Based Physiological Measurement
Nicky Nirlipta Sahoo, VS Sachidanand, Matcha Naga Gayathri +4
Camera-based physiological monitoring, such as remote photoplethysmography (rPPG), captures subtle variations in skin optical properties caused by pulsatile blood volume changes us…
A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction
Balamurali Murugesan, Sriprabha Ramanarayanan, Sricharan Vijayarangan +3
Deep learning networks have shown promising results in fast magnetic resonance imaging (MRI) reconstruction. In our work, we develop deep networks to further improve the quantitati…
MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Prediction
Sriprabha Ramanarayanan, Balamurali Murugesan, Keerthi Ram +1
Convolutional Neural network-based MR reconstruction methods have shown to provide fast and high quality reconstructions. A primary drawback with a CNN-based model is that it lacks…
RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG
Sricharan Vijayarangan, Vignesh R, Balamurali Murugesan +3
Automatic detection of R-peaks in an Electrocardiogram signal is crucial in a multitude of applications including Heart Rate Variability (HRV) analysis and Cardio Vascular Disease(…
Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification
Sricharan Vijayarangan, Balamurali Murugesan, Vignesh R +3
Cardiac arrhythmia is a prevalent and significant cause of morbidity and mortality among cardiac ailments. Early diagnosis is crucial in providing intervention for patients sufferi…
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow
Balamurali Murugesan, Sricharan Vijayarangan, Kaushik Sarveswaran +2
Deep learning networks are being developed in every stage of the MRI workflow and have provided state-of-the-art results. However, this has come at the cost of increased computatio…
DC-WCNN: A deep cascade of wavelet based convolutional neural networks for MR Image Reconstruction
Sriprabha Ramanarayanan, Balamurali Murugesan, Keerthi Ram +1
Several variants of Convolutional Neural Networks (CNN) have been developed for Magnetic Resonance (MR) image reconstruction. Among them, U-Net has shown to be the baseline archite…
A context based deep learning approach for unbalanced medical image segmentation
Balamurali Murugesan, Kaushik Sarveswaran, Vijaya Raghavan S +3
Automated medical image segmentation is an important step in many medical procedures. Recently, deep learning networks have been widely used for various medical image segmentation…
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
José Ignacio Orlando, Huazhu Fu, João Barbossa Breda +28
Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modali…
Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction
Balamurali Murugesan, Vijaya Raghavan S, Kaushik Sarveswaran +2
Magnetic resonance imaging (MRI) is one of the best medical imaging modalities as it offers excellent spatial resolution and soft-tissue contrast. But, the usage of MRI is limited…
Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation
Balamurali Murugesan, Kaushik Sarveswaran, Sharath M Shankaranarayana +3
For the task of medical image segmentation, fully convolutional network (FCN) based architectures have been extensively used with various modifications. A rising trend in these arc…
Deep Network for Capacitive ECG Denoising
Vignesh Ravichandran, Balamurali Murugesan, Sharath M Shankaranarayana +4
Continuous monitoring of cardiac health under free living condition is crucial to provide effective care for patients undergoing post operative recovery and individuals with high c…
RespNet: A deep learning model for extraction of respiration from photoplethysmogram
Vignesh Ravichandran, Balamurali Murugesan, Vaishali Balakarthikeyan +5
Respiratory ailments afflict a wide range of people and manifests itself through conditions like asthma and sleep apnea. Continuous monitoring of chronic respiratory ailments is se…
Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation
Balamurali Murugesan, Kaushik Sarveswaran, Sharath M Shankaranarayana +2
Image segmentation is a primary task in many medical applications. Recently, many deep networks derived from U-Net have been extensively used in various medical image segmentation…