Niyun Zhou — accepted AI-conference papers

2021

PiPo-Net: A Semi-automatic and Polygon-based Annotation Method for Pathological Images

Yuqi Fang, Delong Zhu, Niyun Zhou, Li Liu, Jianhua Yao

IROS 2021poster

Metastatic involvement of lymph nodes is one of the most important prognostic variables for many cancers. Several deep learning based algorithms have been developed to segment metastatic regions in pathological images to help predict prognosis. However, the training of these methods requires a large…

Cited by 5 Source ↗Scholar ↗

2020

Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution

Yu Zhao, Fan Yang, Yuqi Fang, Hailing Liu, Niyun Zhou, Jun Zhang +5

CVPR 2020poster

Multiple instance learning (MIL) is a typical weakly-supervised learning method where the label is associated with a bag of instances instead of a single instance. Despite extensive research over past years, effectively deploying MIL remains an open and challenging problem, especially when the commo…

Cited by 215 PDF ↗Scholar ↗