44 citations · 130 across the 28 of their papers we have counts for
8 papers · 1 filter
MoME: Mixture of Multimodal Experts for Cancer Survival Prediction
Conghao Xiong, Hao Chen, Hao Zheng +4
Survival analysis, as a challenging task, requires integrating Whole Slide Images (WSIs) and genomic data for comprehensive decision-making. There are two main challenges in this t…
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…
Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration
Jiashun Chen, Donghuan Lu, Yu Zhang +5
Recently, deep-learning-based approaches have been widely studied for deformable image registration task. However, most efforts directly map the composite image representation to s…
Deep Convolutional Neural Networks for Molecular Subtyping of Gliomas Using Magnetic Resonance Imaging
Dong Wei, Yiming Li, Yinyan Wang +2
Knowledge of molecular subtypes of gliomas can provide valuable information for tailored therapies. This study aimed to investigate the use of deep convolutional neural networks (D…
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network
Shuxin Wang, Shilei Cao, Zhizhong Chai +4
Fully convolutional neural networks have made promising progress in joint liver and liver tumor segmentation. Instead of following the debates over 2D versus 3D networks (for examp…
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images
Hong Liu, Dong Wei, Donghuan Lu +4
Automated surface segmentation of retinal layer is important and challenging in analyzing optical coherence tomography (OCT). Recently, many deep learning based methods have been d…
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation
Zhe Xu, Yixin Wang, Donghuan Lu +6
Semi-supervised learning has substantially advanced medical image segmentation since it alleviates the heavy burden of acquiring the costly expert-examined annotations. Especially,…
Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation
Zhe Xu, Donghuan Lu, Yixin Wang +5
Manually segmenting the hepatic vessels from Computer Tomography (CT) is far more expertise-demanding and laborious than other structures due to the low-contrast and complex morpho…