4 papers
HUR-MACL: High-Uncertainty Region-Guided Multi-Architecture Collaborative Learning for Head and Neck Multi-Organ Segmentation
Xiaoyu Liu, Siwen Wei, Linhao Qu +4
Accurate segmentation of organs at risk in the head and neck is essential for radiation therapy, yet deep learning models often fail on small, complexly shaped organs. While hybrid…
ME-Mamba: Multi-Expert Mamba with Efficient Knowledge Capture and Fusion for Multimodal Survival Analysis
Chengsheng Zhang, Linhao Qu, Xiaoyu Liu +1
Survival analysis using whole-slide images (WSIs) is crucial in cancer research. Despite significant successes, pathology images typically only provide slide-level labels, which hi…
FANCL: Feature-Guided Attention Network with Curriculum Learning for Brain Metastases Segmentation
Zijiang Liu, Xiaoyu Liu, Linhao Qu +1
Accurate segmentation of brain metastases (BMs) in MR image is crucial for the diagnosis and follow-up of patients. Methods based on deep convolutional neural networks (CNNs) have…
Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation
Xiaoyu Liu, Linhao Qu, Ziyue Xie +2
The task of labeling multiple organs for segmentation is a complex and time-consuming process, resulting in a scarcity of comprehensively labeled multi-organ datasets while the eme…