45 citations · 98 across the 10 of their papers we have counts for
10 papers
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…
A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation
Qing Liu, Beiji Zou, Yang Zhao +1
Segmentation of optic disc (OD) and optic cup (OC) is critical in automated fundus image analysis system. Existing state-of-the-arts focus on designing deep neural networks with on…
Dual-attention Focused Module for Weakly Supervised Object Localization
Yukun Zhou, Zailiang Chen, Hailan Shen +3
The research on recognizing the most discriminative regions provides referential information for weakly supervised object localization with only image-level annotations. However, t…
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…
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…
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…