Search Results for author: Balamurali Murugesan

Found 17 papers, 9 papers with code

Calibrating Segmentation Networks with Margin-based Label Smoothing

1 code implementation9 Sep 2022 Balamurali Murugesan, Bingyuan Liu, Adrian Galdran, Ismail Ben Ayed, Jose Dolz

Following our observations, we propose a simple and flexible generalization based on inequality constraints, which imposes a controllable margin on logit distances.

Image Segmentation Medical Image Segmentation +1

MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Prediction

1 code implementation MIDL 2019 Sriprabha Ramanarayanan, Balamurali Murugesan, Keerthi Ram, Mohanasankar Sivaprakasam

We propose a multiple acquisition context based network, called MAC-ReconNet for MRI reconstruction, flexible to multiple acquisition contexts and generalizable to unseen contexts for applicability in real scenarios.

Anatomy MRI Reconstruction

Style Transfer based Coronary Artery Segmentation in X-ray Angiogram

no code implementations3 Sep 2021 Supriti Mulay, Keerthi Ram, Balamurali Murugesan, Mohanasankar Sivaprakasam

A deep learning-based edge adaptive instance normalization style transfer technique for segmenting the coronary arteries, is presented in this paper.

Style Transfer

KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow

1 code implementation MIDL 2019 Balamurali Murugesan, Sricharan Vijayarangan, Kaushik Sarveswaran, Keerthi Ram, Mohanasankar Sivaprakasam

In our work, we propose a knowledge distillation (KD) framework for the image to image problems in the MRI workflow in order to develop compact, low-parameter models without a significant drop in performance.

Image Restoration Knowledge Distillation +3

A context based deep learning approach for unbalanced medical image segmentation

1 code implementation8 Jan 2020 Balamurali Murugesan, Kaushik Sarveswaran, Vijaya Raghavan S, Sharath M. Shankaranarayana, Keerthi Ram, Mohanasankar Sivaprakasam

Foreground-background class imbalance is a common occurrence in medical images, and U-Net has difficulty in handling class imbalance because of its cross entropy (CE) objective function.

Image Segmentation Medical Image Segmentation +1

Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction

1 code implementation25 Aug 2019 Balamurali Murugesan, Vijaya Raghavan S, Kaushik Sarveswaran, Keerthi Ram, Mohanasankar Sivaprakasam

Our experiments show that the concept of a context discriminator can be extended to existing GAN based reconstruction models to offer better performance.

MRI Reconstruction

Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation

1 code implementation14 Aug 2019 Balamurali Murugesan, Kaushik Sarveswaran, Sharath M. Shankaranarayana, Keerthi Ram, Jayaraj Joseph, Mohanasankar Sivaprakasam

For the task of medical image segmentation, fully convolutional network (FCN) based architectures have been extensively used with various modifications.

Image Segmentation Medical Image Segmentation +2

RespNet: A deep learning model for extraction of respiration from photoplethysmogram

no code implementations12 Feb 2019 Vignesh Ravichandran, Balamurali Murugesan, Vaishali Balakarthikeyan, Sharath M. Shankaranarayana, Keerthi Ram, Preejith S. P, Jayaraj Joseph, Mohanasankar Sivaprakasam

Recently, due to the widespread adoption of wearable smartwatches with in-built Photoplethysmogram (PPG) sensor, it is being considered as a viable candidate for continuous and unobtrusive respiration monitoring.

Cannot find the paper you are looking for? You can Submit a new open access paper.