108 citations · 269 across the 17 of their papers we have counts for
15 papers · 1 filter
Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging
Heejong Kim, Victor Ion Butoi, Adrian V. Dalca +2
Most state-of-the-art techniques for medical image segmentation rely on deep-learning models. These models, however, are often trained on narrowly-defined tasks in a supervised fas…
Neuralizer: General Neuroimage Analysis without Re-Training
Steffen Czolbe, Adrian V. Dalca
Neuroimage processing tasks like segmentation, reconstruction, and registration are central to the study of neuroscience. Robust deep learning strategies and architectures used to…
Learning Task-Specific Strategies for Accelerated MRI
Zihui Wu, Tianwei Yin, Yu Sun +4
Compressed sensing magnetic resonance imaging (CS-MRI) seeks to recover visual information from subsampled measurements for diagnostic tasks. Traditional CS-MRI methods often separ…
Hyper-Convolutions via Implicit Kernels for Medical Imaging
Tianyu Ma, Alan Q. Wang, Adrian V. Dalca +1
The convolutional neural network (CNN) is one of the most commonly used architectures for computer vision tasks. The key building block of a CNN is the convolutional kernel that ag…
3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images
Sungmin Hong, Razvan Marinescu, Adrian V. Dalca +4
Image synthesis via Generative Adversarial Networks (GANs) of three-dimensional (3D) medical images has great potential that can be extended to many medical applications, such as,…
Unsupervised learning of MRI tissue properties using MRI physics models
Divya Varadarajan, Katherine L. Bouman, Andre van der Kouwe +2
In neuroimaging, MRI tissue properties characterize underlying neurobiology, provide quantitative biomarkers for neurological disease detection and analysis, and can be used to syn…