35 citations · 84 across the 12 of their papers we have counts for
10 papers · 1 filter
Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth
Bo Zhou, Chi Liu, James S. Duncan
A large amount of manual segmentation is typically required to train a robust segmentation network so that it can segment objects of interest in a new imaging modality. The manual…
Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation
Chenyu You, Junlin Yang, Julius Chapiro +1
Deep neural networks have shown exceptional learning capability and generalizability in the source domain when massive labeled data is provided. However, the well-trained models of…
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos +6
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propell…
Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis
Xiaoxiao Li, Yuan Zhou, Nicha C. Dvornek +4
Understanding how certain brain regions relate to a specific neurological disorder has been an important area of neuroimaging research. A promising approach to identify the salient…
Invertible Network for Classification and Biomarker Selection for ASD
Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li +2
Determining biomarkers for autism spectrum disorder (ASD) is crucial to understanding its mechanisms. Recently deep learning methods have achieved success in the classification tas…
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
Xiaoxiao Li, Nicha C. Dvornek, Yuan Zhou +3
Discovering imaging biomarkers for autism spectrum disorder (ASD) is critical to help explain ASD and predict or monitor treatment outcomes. Toward this end, deep learning classifi…