activity
20182021
most citedWeakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

185 citations · 250 across the 10 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV202119 cited

Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images

Jingru Yi, Pengxiang Wu, Hui Tang +7

Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells…

cs.CV20209 cited

Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors

Jingru Yi, Pengxiang Wu, Bo Liu +3

Oriented object detection in aerial images is a challenging task as the objects in aerial images are displayed in arbitrary directions and are usually densely packed. Current orien…

cs.CV20202 cited

Learn distributed GAN with Temporary Discriminators

Hui Qu, Yikai Zhang, Qi Chang +3

In this work, we propose a method for training distributed GAN with sequential temporary discriminators. Our proposed method tackles the challenge of training GAN in the federated…

cs.CV2020185 cited

Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

Hui Qu, Pengxiang Wu, Qiaoying Huang +7

Nuclei segmentation is a fundamental task in histopathology image analysis. Typically, such segmentation tasks require significant effort to manually generate accurate pixel-wise a…

cs.CV2019

Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes

Jingru Yi, Pengxiang Wu, Qiaoying Huang +4

Most existing methods handle cell instance segmentation problems directly without relying on additional detection boxes. These methods generally fails to separate touching cells du…

cs.CV2018

MRI Reconstruction via Cascaded Channel-wise Attention Network

Qiaoying Huang, Dong Yang, Pengxiang Wu +3

We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate. This can practically benefit patient due to reduced time of MRI scan, but it i…