Search Results for author: Mohanasankar Sivaprakasam

Found 32 papers, 18 papers with code

HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstruction

1 code implementation9 Aug 2023 Sriprabha Ramanarayanan, Mohammad Al Fahim, Rahul G. S., Amrit Kumar Jethi, Keerthi Ram, Mohanasankar Sivaprakasam

Experiments reveal that our approach 1) adapts on the fly to various unseen configurations up to 32 coils when trained on lower numbers (i. e. 7 to 11) of randomly varying coils, and to 120 deviated unseen configurations when trained on 18 configurations in a single model, 2) matches the performance of coil configuration-specific models, and 3) outperforms configuration-invariant models with improvement margins of around 1 dB / 0. 03 and 0. 3 dB / 0. 02 in PSNR / SSIM for knee and brain data.

Anatomy MRI Reconstruction +1

Geometric Learning-Based Transformer Network for Estimation of Segmentation Errors

no code implementations9 Aug 2023 Sneha Sree C, Mohammad Al Fahim, Keerthi Ram, Mohanasankar Sivaprakasam

We propose a graph neural network-based transformer based on the Nodeformer architecture to measure and classify the segmentation errors at any point.

Image Segmentation Segmentation +1

SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction

1 code implementation8 Aug 2023 Rahul G. S., Sriprabha Ramnarayanan, Mohammad Al Fahim, Keerthi Ram, Preejith S. P, Mohanasankar Sivaprakasam

The self-attention mechanism of the transformer enables transformers to capture long-range dependencies in the images, which might be desirable for accelerated MRI image reconstruction as the effect of undersampling is non-local in the image domain.

Computational Efficiency Image Reconstruction +2

Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworks

1 code implementation13 Jul 2023 Sriprabha Ramanarayanan, Arun Palla, Keerthi Ram, Mohanasankar Sivaprakasam

The hypernetworks and the reconstruction network in the GBML setting provide discriminative mode-specific features and low-level image features, respectively.

Inductive Bias Meta-Learning +2

Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning

no code implementations13 Apr 2023 Arun Palla, Sriprabha Ramanarayanan, Keerthi Ram, Mohanasankar Sivaprakasam

Conventional deep learning methods deal with removing a specific type of artifact, leading to separately trained models for each artifact type that lack the shared knowledge generalizable across artifacts.

Meta-Learning SSIM

SFT-KD-Recon: Learning a Student-friendly Teacher for Knowledge Distillation in Magnetic Resonance Image Reconstruction

1 code implementation11 Apr 2023 Matcha Naga Gayathri, Sriprabha Ramanarayanan, Mohammad Al Fahim, Rahul G S, Keerthi Ram, Mohanasankar Sivaprakasam

We propose SFT-KD-Recon, a student-friendly teacher training approach along with the student as a prior step to KD to make the teacher aware of the structure and capacity of the student and enable aligning the representations of the teacher with the student.

Image Reconstruction Knowledge Distillation

EEG aided boosting of single-lead ECG based sleep staging with Deep Knowledge Distillation

1 code implementation IEEE 2022 Vaibhav Joshi, Sricharan V, Preejith SP, Mohanasankar Sivaprakasam

In order to take advantage of both the modalities, transferring knowledge from EEG to ECG is a reasonable approach, ultimately boosting the performance of ECG based sleep staging.

ECG based Sleep Staging EEG +3

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

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

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

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.

Coronary Artery Segmentation Segmentation +1

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

1 code implementation10 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.

Image Segmentation Medical Image Segmentation +2

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.

Generative Adversarial Network 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

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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