1.1k citations · 1.5k across the 36 of their papers we have counts for
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Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation
Chenyu You, Weicheng Dai, Yifei Min +2
Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation. Existing approaches typically assume a balanced class d…
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation
Chenyu You, Yuan Zhou, Ruihan Zhao +2
Automated segmentation in medical image analysis is a challenging task that requires a large amount of manually labeled data. However, most existing learning-based approaches usual…
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…
Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation
Chenyu You, Ruihan Zhao, Lawrence Staib +1
Contrastive learning (CL) aims to learn useful representation without relying on expert annotations in the context of medical image segmentation. Existing approaches mainly contras…
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…
Repetitive Motion Estimation Network: Recover cardiac and respiratory signal from thoracic imaging
Xiaoxiao Li, Vivek Singh, Yifan Wu +3
Tracking organ motion is important in image-guided interventions, but motion annotations are not always easily available. Thus, we propose Repetitive Motion Estimation Network (RME…
Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI
Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang +2
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the d…
Learning-based Regularization for Cardiac Strain Analysis with Ability for Domain Adaptation
Allen Lu, Nripesh Parajuli, Maria Zontak +10
Reliable motion estimation and strain analysis using 3D+time echocardiography (4DE) for localization and characterization of myocardial injury is valuable for early detection and t…
Flow Network Tracking for Spatiotemporal and Periodic Point Matching: Applied to Cardiac Motion Analysis
Nripesh Parajuli, Allen Lu, Kevinminh Ta +9
The accurate quantification of left ventricular (LV) deformation/strain shows significant promise for quantitatively assessing cardiac function for use in diagnosis and therapy pla…