Our work "Automatic Identification Of Pain-Associated Imaging Abnormalities From Knee MRI Imaging By Deep Generative Networks" was recently presented in OARSI 2023 World Congress on Osteoarthritis, and selected as the Highest Rated Abstract. In this research, we used deep learning techniques to identify imaging abnormalities associated with pain from magnetic resonance imaging (MRI) of knees with symptoms of symptoms of osteoarthritis pain. Read more
Our work "Expansion Microscopy Imaging Isotropic Restoration by Unsupervised Deep Learning" has recently been accepted in Medical Imaging with Deep Learning (MIDL 2023) and to be presented in Nashville, TN. In this study, we developed a single-scale deconvolution model for extracting multi-scale deconvoluted response (MDR) from the volumes of microscopy images of neurons and generative models to translate images between the lateral and axial views in order to achieve isotropic imaging volume restoration. Read more.
