47 citations · 119 across the 36 of their papers we have counts for
22 papers · 1 filter
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…
AAD-DCE: An Aggregated Multimodal Attention Mechanism for Early and Late Dynamic Contrast Enhanced Prostate MRI Synthesis
Divya Bharti, Sriprabha Ramanarayanan, Sadhana S +5
Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a medical imaging technique that plays a crucial role in the detailed visualization and identification of tissue p…
DCE-FORMER: A Transformer-based Model With Mutual Information And Frequency-based Loss Functions For Early And Late Response Prediction In Prostate DCE-MRI
Sadhana S, Sriprabha Ramanarayanan, Arunima Sarkar +3
Dynamic Contrast Enhanced Magnetic Resonance Imaging aids in the detection and assessment of tumor aggressiveness by using a Gadolinium-based contrast agent (GBCA). However, GBCA i…
Geometric Learning-Based Transformer Network for Estimation of Segmentation Errors
Sneha Sree C, Mohammad Al Fahim, Keerthi Ram +1
Many segmentation networks have been proposed for 3D volumetric segmentation of tumors and organs at risk. Hospitals and clinical institutions seek to accelerate and minimize the e…
HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstruction
Sriprabha Ramanarayanan, Mohammad Al Fahim, Rahul G. S. +3
Parallel imaging, a fast MRI technique, involves dynamic adjustments based on the configuration i.e. number, positioning, and sensitivity of the coils with respect to the anatomy u…
SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction
Rahul G. S., Sriprabha Ramnarayanan, Mohammad Al Fahim +3
Transformers have emerged as viable alternatives to convolutional neural networks owing to their ability to learn non-local region relationships in the spatial domain. The self-att…
Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworks
Sriprabha Ramanarayanan, Arun Palla, Keerthi Ram +1
Meta-learning has recently been an emerging data-efficient learning technique for various medical imaging operations and has helped advance contemporary deep learning models. Furth…
Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning
Arun Palla, Sriprabha Ramanarayanan, Keerthi Ram +1
Magnetic Resonance (MR) images suffer from various types of artifacts due to motion, spatial resolution, and under-sampling. Conventional deep learning methods deal with removing a…
SFT-KD-Recon: Learning a Student-friendly Teacher for Knowledge Distillation in Magnetic Resonance Image Reconstruction
Matcha Naga Gayathri, Sriprabha Ramanarayanan, Mohammad Al Fahim +3
Deep cascaded architectures for magnetic resonance imaging (MRI) acceleration have shown remarkable success in providing high-quality reconstruction. However, as the number of casc…
A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRI
Ayantika Das, Arun Palla, Keerthi Ram +1
Anomaly detection in MRI is of high clinical value in imaging and diagnosis. Unsupervised methods for anomaly detection provide interesting formulations based on reconstruction or…
Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit
Nicky Nirlipta Sahoo, Balamurali Murugesan, Ayantika Das +5
Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, makin…
MIST GAN: Modality Imputation Using Style Transfer for MRI
Jaya Chandra Raju, Kompella Subha Gayatri, Keerthi Ram +3
MRI entails a great amount of cost, time and effort for the generation of all the modalities that are recommended for efficient diagnosis and treatment planning. Recent advancement…
Early Detection of Retinopathy of Prematurity stage using Deep Learning approach
Supriti Mulay, Keerthi Ram, Mohanasankar Sivaprakasam +1
Retinopathy of Prematurity (ROP) is a fibrovascular proliferative disorder, which affects the developing peripheral retinal vasculature of premature infants. Early detection of ROP…
Style Transfer based Coronary Artery Segmentation in X-ray Angiogram
Supriti Mulay, Keerthi Ram, Balamurali Murugesan +1
X-ray coronary angiography (XCA) is a principal approach employed for identifying coronary disorders. Deep learning-based networks have recently shown tremendous promise in the dia…
Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images
Madhu Mithra K K, Sriprabha Ramanarayanan, Keerthi Ram +1
Magnetic Resonance Imaging (MRI) is a valuable clinical diagnostic modality for spine pathologies with excellent characterization for infection, tumor, degenerations, fractures and…
Monocular Retinal Depth Estimation and Joint Optic Disc and Cup Segmentation using Adversarial Networks
Sharath M Shankaranarayana, Keerthi Ram, Kaushik Mitra +1
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 bound…
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…
Liver Segmentation from Multimodal Images using HED-Mask R-CNN
Supriti Mulay, Deepika G, Jeevakala S +2
Precise segmentation of the liver is critical for computer-aided diagnosis such as pre-evaluation of the liver for living donor-based transplantation surgery. This task is challeng…
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…
Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation
Sharath M Shankaranarayana, Keerthi Ram, Kaushik Mitra +1
Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH). The color fundus image (CFI) is the mo…