activity
20192023
most citedSignet Ring Cell Detection With a Semi-supervised Learning Framework

16 citations · 36 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2023★ 4 cited

Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain Translation with Inconsistent Groundtruth Image Pairs

Fangda Li, Zhiqiang Hu, Wen Chen +1

Immunohistochemical (IHC) staining highlights the molecular information critical to diagnostics in tissue samples. However, compared to H&E staining, IHC staining can be much more…

cs.CV2022

Contrastive and Selective Hidden Embeddings for Medical Image Segmentation

Zhuowei Li, Zihao Liu, Zhiqiang Hu +5

Medical image segmentation has been widely recognized as a pivot procedure for clinical diagnosis, analysis, and treatment planning. However, the laborious and expensive annotation…

cs.CV2021★ 1 cited

Multi-frame Collaboration for Effective Endoscopic Video Polyp Detection via Spatial-Temporal Feature Transformation

Lingyun Wu, Zhiqiang Hu, Yuanfeng Ji +2

Precise localization of polyp is crucial for early cancer screening in gastrointestinal endoscopy. Videos given by endoscopy bring both richer contextual information as well as mor…

cs.CV2020★ 2 cited

Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets

Rui Huang, Yuanjie Zheng, Zhiqiang Hu +2

Multi-organ segmentation has extensive applications in many clinical applications. To segment multiple organs of interest, it is generally quite difficult to collect full annotatio…

cs.CV2020★ 13 cited

A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging

Zhaohan Xiong, Qing Xia, Zhiqiang Hu +41

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial fir…

cs.CV2019

Accurate Nuclear Segmentation with Center Vector Encoding

Jiahui Li, Zhiqiang Hu, Shuang Yang

Nuclear segmentation is important and frequently demanded for pathology image analysis, yet is also challenging due to nuclear crowdedness and possible occlusion. In this paper, we…