858 citations · 885 across the 11 of their papers we have counts for
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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…