research
∙
10/14/2022
Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning
Although supervised learning has enabled high performance for image segm...
share
research
∙
07/18/2022
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
Unrolled neural networks have recently achieved state-of-the-art acceler...
research
∙
04/21/2022
Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction
Unrolled neural networks have enabled state-of-the-art reconstruction pe...
research
∙
03/14/2022
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation
Magnetic resonance imaging (MRI) is a cornerstone of modern medical imag...
research
∙
09/30/2021
Noise2Recon: A Semi-Supervised Framework for Joint MRI Reconstruction and Denoising
Deep learning (DL) has shown promise for faster, high quality accelerate...
research
∙
04/29/2020
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizin...
research
∙
02/05/2019
