3 citations · 5 across the 6 of their papers we have counts for
6 papers
Diffusion Autoencoder for Unsupervised Artifact Restoration in Handheld Fundus Images
Mathumetha Palani, Kavya Puthumana, Ayantika Das +1
The advent of handheld fundus imaging devices has made ophthalmologic diagnosis and disease screening more accessible, efficient, and cost-effective. However, images captured from…
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…
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…
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…