3 citations · 4 across the 6 of their papers we have counts for
6 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…
Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervision
Linhao Qu, Shiman Li, Xiaoyuan Luo +4
Computer-aided Whole Slide Image (WSI) classification has the potential to enhance the accuracy and efficiency of clinical pathological diagnosis. It is commonly formulated as a Mu…
SP : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation
Shiman Li, Jiayue Zhao, Shaolei Liu +3
Deep learning-based medical image segmentation helps assist diagnosis and accelerate the treatment process while the model training usually requires large-scale dense annotation da…
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…
A comprehensive survey on deep active learning in medical image analysis
Haoran Wang, Qiuye Jin, Shiman Li +3
Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost…