1 code implementation • 13 Mar 2024 • Asad Aali, Giannis Daras, Brett Levac, Sidharth Kumar, Alexandros G. Dimakis, Jonathan I. Tamir
We open-source our code and the trained Ambient Diffusion MRI models: https://github. com/utcsilab/ambient-diffusion-mri .
no code implementations • 2 May 2023 • Asad Aali, Marius Arvinte, Sidharth Kumar, Jonathan I. Tamir
We present SURE-Score: an approach for learning score-based generative models using training samples corrupted by additive Gaussian noise.
1 code implementation • 1 Nov 2022 • Brett Levac, Sidharth Kumar, Ajil Jalal, Jonathan I. Tamir
In this work we propose a framework for jointly reconstructing highly sub-sampled MRI data while estimating patient motion using diffusion based generative models.
no code implementations • 1 Jul 2022 • Brett Levac, Sidharth Kumar, Sofia Kardonik, Jonathan I. Tamir
Magnetic Resonance Imaging (MRI) is a widely used medical imaging modality boasting great soft tissue contrast without ionizing radiation, but unfortunately suffers from long acquisition times.
1 code implementation • 21 Jun 2022 • Ali Lotfi Rezaabad, Sidharth Kumar, Sriram Vishwanath, Jonathan I. Tamir
Pretraining on a large source data set and fine-tuning on the target samples is prone to overfitting in the few-shot regime, where only a small number of target samples are available.
no code implementations • 3 May 2022 • Kalina P. Slavkova, Julie C. DiCarlo, Viraj Wadhwa, Chengyue Wu, John Virostko, Sidharth Kumar, Thomas E. Yankeelov, Jonathan I. Tamir
We conclude that the use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.
no code implementations • 11 Oct 2020 • Ryan M. Dreifuerst, Andrew Graff, Sidharth Kumar, Clive Unger, Dylan Bray
This paper presents a novel method for classifying radio frequency (RF) devices from their transmission signals.