20 citations · 74 across the 9 of their papers we have counts for
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cs.CV2019★ 20 cited
Weakly supervised segmentation from extreme points
Holger Roth, Ling Zhang, Dong Yang +4
Annotation of medical images has been a major bottleneck for the development of accurate and robust machine learning models. Annotation is costly and time-consuming and typically r…
cs.CV2019★ 1 cited
Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network
Ziyue Xu, Xiaosong Wang, Hoo-Chang Shin +5
Radiogenomic map linking image features and gene expression profiles is useful for noninvasively identifying molecular properties of a particular type of disease. Conventionally, s…
cs.CV2019★ 7 cited
Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering With Graph Convolutional Neural Network
Ling Zhang, Zhigang Zhu
To alleviate the cost of collecting and annotating large-scale point cloud datasets, we propose an unsupervised learning approach to learn features from unlabeled point cloud "3D o…