Ke Yan

971 total citations · 1 hit paper
11 papers, 666 citations indexed

About

Ke Yan is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Ophthalmology. According to data from OpenAlex, Ke Yan has authored 11 papers receiving a total of 666 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 4 papers in Radiology, Nuclear Medicine and Imaging and 1 paper in Ophthalmology. Recurrent topics in Ke Yan's work include Advanced Neural Network Applications (6 papers), Advanced Image and Video Retrieval Techniques (5 papers) and Visual Attention and Saliency Detection (5 papers). Ke Yan is often cited by papers focused on Advanced Neural Network Applications (6 papers), Advanced Image and Video Retrieval Techniques (5 papers) and Visual Attention and Saliency Detection (5 papers). Ke Yan collaborates with scholars based in Australia, China and United States. Ke Yan's co-authors include Xiaoou Tang, Jing Feng, Xiuying Wang, Wei Zeng, Yaowei Wang, Yonghong Tian, Tiejun Huang, Jinman Kim, Dagan Feng and Changyang Li and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Cybernetics and Computer Methods and Programs in Biomedicine.

In The Last Decade

Ke Yan

11 papers receiving 644 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The Design of High-Level Features for Photo Quality Assessment

2006 425 citations Ke Yan, Xiaoou Tang et al. profile →

Peers

Countries citing papers authored by Ke Yan

Since Specialization

Citations

This map shows the geographic impact of Ke Yan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ke Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ke Yan more than expected).

Fields of papers citing papers by Ke Yan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ke Yan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ke Yan. The network helps show where Ke Yan may publish in the future.

Co-authorship network of co-authors of Ke Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Ke Yan. A scholar is included among the top collaborators of Ke Yan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ke Yan. Ke Yan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

#	Work	Indexed citations
1	Deep Cognitive Gate: Resembling Human Cognition for Saliency Detection 2021 ·IEEE Transactions on Pattern Analysis and Machine Intelligence ·Ke Yan, Xiuying Wang, Jinman Kim, Wangmeng Zuo, Dagan Feng	6
2	A New Aggregation of DNN Sparse and Dense Labeling for Saliency Detection 2020 ·IEEE Transactions on Cybernetics ·Ke Yan, Xiuying Wang, Jinman Kim, Dagan Feng	13
3	Deep multi-scale feature fusion for pancreas segmentation from CT images 2020 ·International Journal of Computer Assisted Radiology and Surgery ·(unknown), Xiuying Wang, Ke Yan, Jiangbin Zheng	10
4	A propagation-DNN: Deep combination learning of multi-level features for MR prostate segmentation 2018 ·Computer Methods and Programs in Biomedicine ·Ke Yan, Xiuying Wang, Jinman Kim, Mohamed Khadra, Michael Fulham, Dagan Feng	34
5	Exploiting Multi-grain Ranking Constraints for Precisely Searching Visually-similar Vehicles 2017 ·Ke Yan, Yonghong Tian, Yaowei Wang, Wei Zeng, Tiejun Huang	103
6	Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy 2017 ·Biomedical Optics Express ·Yupeng Xu, Ke Yan, Jinman Kim, Xiuying Wang, Changyang Li, Li Su, Suqin Yu, Xun Xu, (unknown)	49
7	Automatic prostate segmentation on MR images with deep network and graph model 2016 ·PubMed ·Ke Yan, Changyang Li, Xiuying Wang, Ang Li, Yuchen Yuan, Dagan Feng, Mohamed Khadra, Jinman Kim	15
8	Comprehensive autoencoder for prostate recognition on MR images 2016 ·The Sydney eScholarship Repository (The University of Sydney) ·Ke Yan, Changyang Li, Xiuying Wang, Yuchen Yuan, Ang Li, Jinman Kim, Biao Li, Dagan Feng	6
9	Multilevel affinity graph for unsupervised image segmentation 2016 ·(unknown), Xiuying Wang, Ke Yan, Changyang Li, Dagan Feng	3
10	Adaptive background search and foreground estimation for saliency detection via comprehensive autoencoder 2016 ·The Sydney eScholarship Repository (The University of Sydney) ·Ke Yan, Changyang Li, Xiuying Wang, Ang Li, Yuchen Yuan, Jinman Kim, Dagan Feng	2
11	The Design of High-Level Features for Photo Quality Assessment breakdown → 2006 ·Ke Yan, Xiaoou Tang, Jing Feng	425

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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