17 citations · 21 across the 3 of their papers we have counts for
5 papers
InJecteD: Analyzing Trajectories and Drift Dynamics in Denoising Diffusion Probabilistic Models for 2D Point Cloud Generation
Sanyam Jain, Khuram Naveed, Illia Oleksiienko +2
This work introduces InJecteD, a framework for interpreting Denoising Diffusion Probabilistic Models (DDPMs) by analyzing sample trajectories during the denoising process of 2D poi…
Impact of Labeling Inaccuracy and Image Noise on Tooth Segmentation in Panoramic Radiographs using Federated, Centralized and Local Learning
Johan Andreas Balle Rubak, Khuram Naveed, Sanyam Jain +3
Objectives: Federated learning (FL) may mitigate privacy constraints, heterogeneous data quality, and inconsistent labeling in dental diagnostic AI. We compared FL with centralized…
PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolution
Sanyam Jain, Bruna Neves de Freitas, Andreas Basse-OConnor +2
Interest in generating realistic synthetic medical images has grown in recent years. Such datasets can mitigate the scarcity of public data for artificial intelligence research and…
NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs
Khuram Naveed, Bruna Neves de Freitas, Ruben Pauwels
Convolutional denoising autoencoders (DAEs) are powerful tools for image restoration. However, they inherit a key limitation of convolutional neural networks (CNNs): they tend to r…
Federated learning, ethics, and the double black box problem in medical AI
Joshua Hatherley, Anders Søgaard, Angela Ballantyne +1
Federated learning (FL) is a machine learning approach that allows multiple devices or institutions to collaboratively train a model without sharing their local data with a third-p…