7 citations · 7 across the 1 of their papers we have counts for
3 papers
cs.CV2019★ 7 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
Efficient Misalignment-Robust Multi-Focus Microscopical Images Fusion
Yixiong Liang, Yuan Mao, Zhihong Tang +3
In this paper we propose a very efficient method to fuse the unregistered multi-focus microscopical images based on the speed-up robust features (SURF). Our method follows the pipe…
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…