activity
20112019
most citedA Novel Automation-Assisted Cervical Cancer Reading Method Based on Convolutional Neural Network

7 citations · 13 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV20197 cited

A Novel Automation-Assisted Cervical Cancer Reading Method Based on Convolutional Neural Network

Yao Xiang, Wanxin Sun, Changli Pan +3

While most previous automation-assisted reading methods can improve efficiency, their performance often relies on the success of accurate cell segmentation and hand-craft feature e…

cs.CV2018

Scale-Invariant Structure Saliency Selection for Fast Image Fusion

Yixiong Liang, Yuan Mao, Jiazhi Xia +2

In this paper, we present a fast yet effective method for pixel-level scale-invariant image fusion in spatial domain based on the scale-space theory. Specifically, we propose a sca…

cs.CV2018

Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario

Yixiong Liang, Zhihong Tang, Meng Yan +3

Automated detection of cervical cancer cells or cell clumps has the potential to significantly reduce error rate and increase productivity in cervical cancer screening. However, mo…

cs.CV20114 cited

Feature Selection via Sparse Approximation for Face Recognition

Yixiong Liang, Lei Wang, Yao Xiang +1

Inspired by biological vision systems, the over-complete local features with huge cardinality are increasingly used for face recognition during the last decades. Accordingly, featu…

cs.CV20112 cited

Multi-task GLOH feature selection for human age estimation

Yixiong Liang, Lingbo Liu, Ying Xu +2

In this paper, we propose a novel age estimation method based on GLOH feature descriptor and multi-task learning (MTL). The GLOH feature descriptor, one of the state-of-the-art fea…