43 citations · 114 across the 25 of their papers we have counts for
6 papers · 1 filter
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations
Hong Liu, Dong Wei, Donghuan Lu +3
Layer segmentation is important to quantitative analysis of retinal optical coherence tomography (OCT). Recently, deep learning based methods have been developed to automate this t…
Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views
Dong Wei, Yawen Huang, Donghuan Lu +2
Background: View planning for the acquisition of cardiac magnetic resonance (CMR) imaging remains a demanding task in clinical practice. Purpose: Existing approaches to its automat…
You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray
Jinghan Sun, Dong Wei, Zhe Xu +4
Chest X-ray (CXR) anatomical abnormality detection aims at localizing and characterising cardiopulmonary radiological findings in the radiographs, which can expedite clinical workf…
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities
Hong Liu, Dong Wei, Donghuan Lu +3
Multimodal magnetic resonance imaging (MRI) provides complementary information for sub-region analysis of brain tumors. Plenty of methods have been proposed for automatic brain tum…
RECIST Weakly Supervised Lesion Segmentation via Label-Space Co-Training
Lianyu Zhou, Dong Wei, Donghuan Lu +3
As an essential indicator for cancer progression and treatment response, tumor size is often measured following the response evaluation criteria in solid tumors (RECIST) guideline…
MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation
Munan Ning, Donghuan Lu, Yujia Xie +6
Unsupervised domain adaption has been widely adopted in tasks with scarce annotated data. Unfortunately, mapping the target-domain distribution to the source-domain unconditionally…