activity
20182021
most citedWeakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation

45 citations · 45 across the 3 of their papers we have counts for

collaborators

5 papers

eess.IV2021

A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images

Zijie Chen, Cheng Li, Junjun He +5

Radiation therapy (RT) is widely employed in the clinic for the treatment of head and neck (HaN) cancers. An essential step of RT planning is the accurate segmentation of various o…

eess.IV2021

Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation

Junjun He, Jin Ye, Cheng Li +5

Recent studies have witnessed the effectiveness of 3D convolutions on segmenting volumetric medical images. Compared with the 2D counterparts, 3D convolutions can capture the spati…

eess.IV2021

SS-CADA: A Semi-Supervised Cross-Anatomy Domain Adaptation for Coronary Artery Segmentation

Jingyang Zhang, Ran Gu, Guotai Wang +2

The segmentation of coronary arteries by convolutional neural network is promising yet requires a large amount of labor-intensive manual annotations. Transferring knowledge from re…

cs.CV202045 cited

Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation

Jingyang Zhang, Guotai Wang, Hongzhi Xie +4

The segmentation of coronary arteries in X-ray angiograms by convolutional neural networks (CNNs) is promising yet limited by the requirement of precisely annotating all pixels in…

cs.LG2018

A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteria

Yu Zhao, Zhenhui Shi, Jingyang Zhang +2

Multiple query criteria active learning (MQCAL) methods have a higher potential performance than conventional active learning methods in which only one criterion is deployed for sa…