47 citations · 119 across the 36 of their papers we have counts for
39 papers
SHFormer: Dynamic Spectral Filtering Convolutional Neural Network and High-pass Kernel Generation Transformer for Adaptive MRI Reconstruction
Sriprabha Ramanarayanan, Rahul G. S., Mohammad Al Fahim +3
Attention Mechanism (AM) selectively focuses on essential information for imaging tasks and captures relationships between distant pixel neighborhoods to compute feature representa…
Align-cDAE: Alzheimer's Disease Progression Modeling with Attention-Aligned Conditional Diffusion Auto-Encoder
Ayantika Das, Keerthi Ram, Mohanasankar Sivaprakasam
Generative AI framework-based modeling and prediction of longitudinal human brain images offer an efficient mechanism to track neurodegenerative progression, essential for the asse…
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…
CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training
Pralaypati Ta, Sriram Venkatesaperumal, Keerthi Ram +1
The functions of different regions of the human brain are closely linked to their distinct cytoarchitecture, which is defined by the spatial arrangement and morphology of the cells…
AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders
Ayantika Das, Arunima Sarkar, Keerthi Ram +1
Generative modeling frameworks have emerged as an effective approach to capture high-dimensional image distributions from large datasets without requiring domain-specific knowledge…
PosDiffAE: Position-aware Diffusion Auto-encoder For High-Resolution Brain Tissue Classification Incorporating Artifact Restoration
Ayantika Das, Moitreya Chaudhuri, Koushik Bhat +3
Denoising diffusion models produce high-fidelity image samples by capturing the image distribution in a progressive manner while initializing with a simple distribution and compoun…
Entity-Augmented Neuroscience Knowledge Retrieval Using Ontology and Semantic Understanding Capability of LLM
Pralaypati Ta, Sriram Venkatesaperumal, Keerthi Ram +1
Neuroscience research publications encompass a vast wealth of knowledge. Accurately retrieving existing information and discovering new insights from this extensive literature is e…
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…
Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs
Bhumika Gupta, Pralaypati Ta, Keerthi Ram +1
The updated recommendations on diagnostic procedures and treatment pathways for a medical condition are documented as graphical flows in Clinical Practice Guidelines (CPGs). For ef…
Knowledge Models for Cancer Clinical Practice Guidelines : Construction, Management and Usage in Question Answering
Pralaypati Ta, Bhumika Gupta, Arihant Jain +3
An automated knowledge modeling algorithm for Cancer Clinical Practice Guidelines (CPGs) extracts the knowledge contained in the CPG documents and transforms it into a programmatic…
A Hybrid-Layered System for Image-Guided Navigation and Robot Assisted Spine Surgeries
Suhail Ansari T, Vivek Maik, Minhas Naheem +3
In response to the growing demand for precise and affordable solutions for Image-Guided Spine Surgery (IGSS), this paper presents a comprehensive development of a Robot-Assisted an…
A Hybrid-Layered System for Image-Guided Navigation and Robot Assisted Spine Surgery
Suhail Ansari T, Vivek Maik, Minhas Naheem +3
In response to the growing demand for precise and affordable solutions for Image-Guided Spine Surgery (IGSS), this paper presents a comprehensive development of a Robot-Assisted an…
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…
Automated Knowledge Modeling for Cancer Clinical Practice Guidelines
Pralaypati Ta, Bhumika Gupta, Arihant Jain +4
Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to new evidence generated by active research. Currently, CPGs are primarily published in a document forma…
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…