29 citations · 47 across the 8 of their papers we have counts for
8 papers
Topology-Preserving Automatic Labeling of Coronary Arteries via Anatomy-aware Connection Classifier
Zhixing Zhang, Ziwei Zhao, Dong Wang +4
Automatic labeling of coronary arteries is an essential task in the practical diagnosis process of cardiovascular diseases. For experienced radiologists, the anatomically predeterm…
Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers
AnLan Sun, Zhao Zhang, Meng Lei +3
Breast ultrasound videos contain richer information than ultrasound images, therefore it is more meaningful to develop video models for this diagnosis task. However, the collection…
Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion Detection
Haojun Yu, Youcheng Li, QuanLin Wu +4
During ultrasonic scanning processes, real-time lesion detection can assist radiologists in accurate cancer diagnosis. However, this essential task remains challenging and underexp…
Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection
Ziwei Zhao, Dong Wang, Yihong Chen +2
Detecting mass in mammogram is significant due to the high occurrence and mortality of breast cancer. In mammogram mass detection, modeling pairwise lesion correspondence explicitl…
PointScatter: Point Set Representation for Tubular Structure Extraction
Dong Wang, Zhao Zhang, Ziwei Zhao +3
This paper explores the point set representation for tubular structure extraction tasks. Compared with the traditional mask representation, the point set representation enjoys its…
Improve bone age assessment by learning from anatomical local regions
Dong Wang, Kexin Zhang, Jia Ding +1
Skeletal bone age assessment (BAA), as an essential imaging examination, aims at evaluating the biological and structural maturation of human bones. In the clinical practice, Tanne…
FocalMix: Semi-Supervised Learning for 3D Medical Image Detection
Dong Wang, Yuan Zhang, Kexin Zhang +1
Applying artificial intelligence techniques in medical imaging is one of the most promising areas in medicine. However, most of the recent success in this area highly relies on lar…
Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems
Tianle Cai, Ruiqi Gao, Jikai Hou +5
First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks. Second-order methods, despite their better con…