activity
20202024
most citedA Novel Dataset and a Deep Learning Method for Mitosis Nuclei Segmentation and Classification

4 citations · 8 across the 4 of their papers we have counts for

collaborators

5 papers

eess.IV2024★ 3 cited

Gland Segmentation Via Dual Encoders and Boundary-Enhanced Attention

Huadeng Wang, Jiejiang Yu, Bingbing Li +4

Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma. However, due to various gland sh…

cs.CV2023

A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch

Huadeng Wang, Hao Xu, Bingbing Li +4

Mitosis detection is one of the challenging problems in computational pathology, and mitotic count is an important index of cancer grading for pathologists. However, current counts…

cs.CV2022★ 4 cited

A Novel Dataset and a Deep Learning Method for Mitosis Nuclei Segmentation and Classification

Huadeng Wang, Zhipeng Liu, Rushi Lan +4

Mitosis nuclei count is one of the important indicators for the pathological diagnosis of breast cancer. The manual annotation needs experienced pathologists, which is very time-co…

cs.CV2022★ 1 cited

Binary Representation via Jointly Personalized Sparse Hashing

Xiaoqin Wang, Chen Chen, Rushi Lan +4

Unsupervised hashing has attracted much attention for binary representation learning due to the requirement of economical storage and efficiency of binary codes. It aims to encode…

eess.IV2020

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

Kai Zhang, Martin Danelljan, Yawei Li +75

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an in…