activity
20162021
most citedUncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation

280 citations · 335 across the 8 of their papers we have counts for

collaborators

14 papers

eess.IV2021

Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study

Dazhou Guo, Jia Ge, Xianghua Ye +22

Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications. Consensus guidelines recommend a set of more than 40 OARs in the head…

cs.CV2020★ 20 cited

Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples

Yingwei Li, Zhuotun Zhu, Yuyin Zhou +4

Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to th…

cs.CV2020★ 4 cited

Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network

Chun-Hung Chao, Zhuotun Zhu, Dazhou Guo +10

Determining the spread of GTV is essential in defining the respective resection or irradiating regions for the downstream workflows of surgical resection and radiotherapy fo…

eess.IV2020★ 3 cited

Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy

Zhuotun Zhu, Dakai Jin, Ke Yan +7

Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance. In radiotherapy, they are…

cs.CV2020★ 280 cited

Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation

Yingda Xia, Dong Yang, Zhiding Yu +7

Although having achieved great success in medical image segmentation, deep learning-based approaches usually require large amounts of well-annotated data, which can be extremely ex…

cs.CV2020★ 7 cited

Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification

Zhuotun Zhu, Ke Yan, Dakai Jin +9

Finding and identifying scatteredly-distributed, small, and critically important objects in 3D oncology images is very challenging. We focus on the detection and segmentation of on…