13 citations · 19 across the 5 of their papers we have counts for
5 papers
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…
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…
Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation
Shiman Li, Haoran Wang, Yucong Meng +2
Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-gui…
TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning
Linhao Qu, Shaolei Liu, Manning Wang +4
Image fusion is a technique to integrate information from multiple source images with complementary information to improve the richness of a single image. Due to insufficient task-…