4 citations · 7 across the 10 of their papers we have counts for
6 papers · 1 filter
Dynamic Pseudo Label Optimization in Point-Supervised Nuclei Segmentation
Ziyue Wang, Ye Zhang, Yifeng Wang +2
Deep learning has achieved impressive results in nuclei segmentation, but the massive requirement for pixel-wise labels remains a significant challenge. To alleviate the annotation…
DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions
Ye Zhang, Yifeng Wang, Zijie Fang +4
Weakly supervised segmentation methods have gained significant attention due to their ability to reduce the reliance on costly pixel-level annotations during model training. Howeve…
Rethinking Attention-Based Multiple Instance Learning for Whole-Slide Pathological Image Classification: An Instance Attribute Viewpoint
Linghan Cai, Shenjin Huang, Ye Zhang +2
Multiple instance learning (MIL) is a robust paradigm for whole-slide pathological image (WSI) analysis, processing gigapixel-resolution images with slide-level labels. As pioneeri…
MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models
Zijie Fang, Yifeng Wang, Ye Zhang +4
Recently, pathological diagnosis has achieved superior performance by combining deep learning models with the multiple instance learning (MIL) framework using whole slide images (W…
SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation
Ye Zhang, Linghan Cai, Ziyue Wang +1
Nuclei instance segmentation in histopathological images is of great importance for biological analysis and cancer diagnosis but remains challenging for two reasons. (1) Similar vi…
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation
Ye Zhang, Ziyue Wang, Yifeng Wang +5
Semi-supervised segmentation methods have demonstrated promising results in natural scenarios, providing a solution to reduce dependency on manual annotation. However, these method…