316 citations · 2.9k across the 50 of their papers we have counts for
7 papers · 1 filter
MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery
Duowen Chen, Yunhao Bai, Wei Shen +3
We propose a novel teacher-student model for semi-supervised multi-organ segmentation. In teacher-student model, data augmentation is usually adopted on unlabeled data to regulariz…
Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whitening
Kang Li, Lequan Yu, Pheng-Ann Heng
Contemporary methods have shown promising results on cardiac image segmentation, but merely in static learning, i.e., optimizing the network once for all, ignoring potential needs…
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning
Fuying Wang, Yuyin Zhou, Shujun Wang +2
Learning medical visual representations directly from paired radiology reports has become an emerging topic in representation learning. However, existing medical image-text joint l…
MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation
Zhenchao Jin, Dongdong Yu, Zehuan Yuan +1
Co-occurrent visual pattern makes context aggregation become an essential paradigm for semantic segmentation.The existing studies focus on modeling the contexts within image while…
Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware Contrastive Learning
Tianling Liu, Wennan Liu, Lequan Yu +3
Preoperative and noninvasive prediction of the meningioma grade is important in clinical practice, as it directly influences the clinical decision making. What's more, brain invasi…
NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation
Zhaohu Xing, Lequan Yu, Liang Wan +2
Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate brain tumors by providing rich complementary information. Previous multi-modal MRI segment…
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training
Cheng Xue, Lequan Yu, Pengfei Chen +2
Deep neural networks have achieved remarkable success in a wide variety of natural image and medical image computing tasks. However, these achievements indispensably rely on accura…