Search Results for author: Mohanasankar Sivaprakasam

Found 20 papers, 10 papers with code

A Deep Learning Based Multitask Network for Respiration Rate Estimation -- A Practical Perspective

1 code implementation13 Dec 2021 Kapil Singh Rathore, Sricharan Vijayarangan, Preejith SP, Mohanasankar Sivaprakasam

The multitasking network consists of a combination of Encoder-Decoder and Encoder-IncResNet, to fetch the average respiration rate and the respiration signal.

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

no code implementations 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.

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

Early Detection of Retinopathy of Prematurity stage using Deep Learning approach

no code implementations3 Sep 2021 Supriti Mulay, Keerthi Ram, Mohanasankar Sivaprakasam, Anand Vinekar

The system was tested on 45 images and reached detection accuracy of 0. 88, showing that deep learning detection with pre-processing by image normalization allows robust detection of ROP in early stages.

Image Enhancement

Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images

no code implementations10 Feb 2021 Madhu Mithra K K, Sriprabha Ramanarayanan, Keerthi Ram, Mohanasankar Sivaprakasam

Magnetic Resonance Imaging (MRI) is a valuable clinical diagnostic modality for spine pathologies with excellent characterization for infection, tumor, degenerations, fractures and herniations.

Image Super-Resolution SSIM

Monocular Retinal Depth Estimation and Joint Optic Disc and Cup Segmentation using Adversarial Networks

no code implementations15 Jul 2020 Sharath M. Shankaranarayana, Keerthi Ram, Kaushik Mitra, Mohanasankar Sivaprakasam

One of the important parameters for the assessment of glaucoma is optic nerve head (ONH) evaluation, which usually involves depth estimation and subsequent optic disc and cup boundary extraction.

Depth Estimation

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.

Medical Image Segmentation

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.

Medical Image Segmentation Multi-Task Learning

PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram

no code implementations21 Mar 2019 Shyam A, Vignesh Ravichandran, Preejith S. P, Jayaraj Joseph, Mohanasankar Sivaprakasam

Traditional machine learning and deep learning approaches rely on tri-axial accelerometer data along with PPG to perform heart rate estimation.

Heart rate estimation Transfer Learning

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.

Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation

no code implementations4 Feb 2019 Sharath M. Shankaranarayana, Keerthi Ram, Kaushik Mitra, Mohanasankar Sivaprakasam

Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH).

Depth Estimation

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