8 citations · 17 across the 11 of their papers we have counts for
11 papers
Score-based Generative Diffusion Models to Synthesize Full-dose FDG Brain PET from MRI in Epilepsy Patients
Jiaqi Wu, Jiahong Ouyang, Farshad Moradi +2
Fluorodeoxyglucose (FDG) PET to evaluate patients with epilepsy is one of the most common applications for simultaneous PET/MRI, given the need to image both brain structure and me…
SOE: SO(3)-Equivariant 3D MRI Encoding
Shizhe He, Magdalini Paschali, Jiahong Ouyang +3
Representation learning has become increasingly important, especially as powerful models have shifted towards learning latent representations before fine-tuning for downstream task…
Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks
Mohammed Baharoon, Waseem Qureshi, Jiahong Ouyang +3
The integration of deep learning systems into healthcare has been hindered by the resource-intensive process of data annotation and the inability of these systems to generalize to…
Contrastive Self-Supervised Learning for Spatio-Temporal Analysis of Lung Ultrasound Videos
Li Chen, Jonathan Rubin, Jiahong Ouyang +24
Self-supervised learning (SSL) methods have shown promise for medical imaging applications by learning meaningful visual representations, even when the amount of labeled data is li…
Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIs
Wei Peng, Tomas Bosschieter, Jiahong Ouyang +4
Generative AI models hold great potential in creating synthetic brain MRIs that advance neuroimaging studies by, for example, enriching data diversity. However, the mainstay of AI…
LSOR: Longitudinally-Consistent Self-Organized Representation Learning
Jiahong Ouyang, Qingyu Zhao, Ehsan Adeli +3
Interpretability is a key issue when applying deep learning models to longitudinal brain MRIs. One way to address this issue is by visualizing the high-dimensional latent spaces ge…