4 citations · 8 across the 5 of their papers we have counts for
5 papers
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…
H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT Images
Jinpeng Lu, Jingyun Chen, Linghan Cai +2
Positron emission tomography (PET) combined with computed tomography (CT) imaging is routinely used in cancer diagnosis and prognosis by providing complementary information. Automa…
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…
A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images
Linghan Cai, Jianhao Huang, Zihang Zhu +2
Fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with computed tomography (CT) is considered the primary solution for detecting some cancers, such as lung cance…