most citedSelf-supervised Training of Graph Convolutional Networks

27 citations · 32 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20201 cited

Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning

Hengtao Guo, Sheng Xu, Bradford Wood +1

Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information. Current methods for 3D v…

cs.CV202027 cited

Self-supervised Training of Graph Convolutional Networks

Qikui Zhu, Bo Du, Pingkun Yan

Graph Convolutional Networks (GCNs) have been successfully applied to analyze non-grid data, where the classical convolutional neural networks (CNNs) cannot be directly used. One s…

cs.CV2020

Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction

Xi Fang, Pingkun Yan

Shortage of fully annotated datasets has been a limiting factor in developing deep learning based image segmentation algorithms and the problem becomes more pronounced in multi-org…

cs.CV2019

OASIS: One-pass aligned Atlas Set for Image Segmentation

Qikui Zhu, Bo Du, Pingkun Yan

Medical image segmentation is a fundamental task in medical image analysis. Despite that deep convolutional neural networks have gained stellar performance in this challenging task…

eess.IV20194 cited

Unified Multi-scale Feature Abstraction for Medical Image Segmentation

Xi Fang, Bo Du, Sheng Xu +2

Automatic medical image segmentation, an essential component of medical image analysis, plays an importantrole in computer-aided diagnosis. For example, locating and segmenting the…