Chaolu Feng

1.3k total citations
53 papers, 505 citations indexed

About

Chaolu Feng is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Chaolu Feng has authored 53 papers receiving a total of 505 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Computer Vision and Pattern Recognition, 22 papers in Radiology, Nuclear Medicine and Imaging and 9 papers in Artificial Intelligence. Recurrent topics in Chaolu Feng's work include Medical Image Segmentation Techniques (20 papers), Advanced Neural Network Applications (8 papers) and Radiomics and Machine Learning in Medical Imaging (7 papers). Chaolu Feng is often cited by papers focused on Medical Image Segmentation Techniques (20 papers), Advanced Neural Network Applications (8 papers) and Radiomics and Machine Learning in Medical Imaging (7 papers). Chaolu Feng collaborates with scholars based in China, United States and Saint Kitts and Nevis. Chaolu Feng's co-authors include Dazhe Zhao, Min Huang, Jinzhu Yang, Chunming Li, Christos Davatzikos, Xiaozhu Lin, Xiaohua Qian, Shaoxiang Zhang, Kun Yu and Wei Li and has published in prestigious journals such as IEEE Transactions on Medical Imaging, Medical Physics and Neurocomputing.

In The Last Decade

Chaolu Feng

50 papers receiving 494 citations

Peers

Chaolu Feng

CVPR

RNMI

NEURO

Ivan Cruz‐Aceves Mexico

Stephen Chang Singapore

Xingqun Qi China

Muyi Sun China

Nawrès Khlifa Tunisia

Shuyue Guan United States

Chenchu Xu China

Osama A. Omer Egypt

Farhan Akram Spain

Pierre-Henri Conze France

Ivan Cruz‐Aceves Mexico

Chaolu Feng

314 ×1.1

CVPR

303 ×1.4

RNMI

113 ×1.2

70 ×1.0

77 ×1.2

NEURO

Citations per year, relative to Chaolu Feng Chaolu Feng (= 1×) peers Ivan Cruz‐Aceves

Countries citing papers authored by Chaolu Feng

Since Specialization

Citations

This map shows the geographic impact of Chaolu Feng'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 Chaolu Feng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chaolu Feng more than expected).

Fields of papers citing papers by Chaolu Feng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chaolu Feng. 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 Chaolu Feng. The network helps show where Chaolu Feng may publish in the future.

Co-authorship network of co-authors of Chaolu Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Chaolu Feng. A scholar is included among the top collaborators of Chaolu Feng 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 Chaolu Feng. Chaolu Feng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Hu, Jun, Yuefeng Wang, Bo Fu, et al.. (2025). A survey of decision-making and planning methods for self-driving vehicles. Frontiers in Neurorobotics. 19. 1451923–1451923. 3 indexed citations

Li, Wei, et al.. (2024). nsDCC: dual-level contrastive clustering with nonuniform sampling for scRNA-seq data analysis. Briefings in Bioinformatics. 25(6). 1 indexed citations

Yang, Jinzhu, et al.. (2024). A spatio-temporal graph convolutional network for ultrasound echocardiographic landmark detection. Medical Image Analysis. 97. 103272–103272. 3 indexed citations

Feng, Chaolu, et al.. (2024). GCReID: Generalized continual person re-identification via meta learning and knowledge accumulation. Neural Networks. 179. 106561–106561. 1 indexed citations

Li, Wei, et al.. (2024). MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis. BMC Medical Imaging. 24(1). 47–47. 2 indexed citations

Wang, Lu, Nan‐Jie Xu, Xiran Jiang, et al.. (2023). A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images. iScience. 26(7). 107005–107005. 7 indexed citations

Jiang, Cong, et al.. (2023). Learning discriminative foreground-and-background features for few-shot segmentation. Multimedia Tools and Applications. 83(18). 55999–56019. 2 indexed citations

Feng, Chaolu, et al.. (2023). Region based level sets for image segmentation: a brief comparative review with a fast model FREEST. Multimedia Tools and Applications. 82(24). 37065–37095. 6 indexed citations

Feng, Chaolu, et al.. (2023). EchoEFNet: Multi-task deep learning network for automatic calculation of left ventricular ejection fraction in 2D echocardiography. Computers in Biology and Medicine. 156. 106705–106705. 30 indexed citations

10.

Li, Wei, Shanshan Wang, Weidong Xie, & Chaolu Feng. (2023). A quantitative heterogeneity analysis approach to molecular subtype recognition of breast cancer in dynamic contrast-enhanced magnetic imaging images from radiomics data. Quantitative Imaging in Medicine and Surgery. 13(7). 4429–4446. 2 indexed citations

11.

Chen, Binghua, Dong‐Aolei An, Yihong Zhang, et al.. (2023). A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data. European Radiology. 33(12). 8477–8487. 3 indexed citations

12.

Li, Shulin, Chaolu Feng, Kun Yu, et al.. (2022). Critical review of human cardiac magnetic resonance image super resolution reconstruction based on deep learning method. Journal of Image and Graphics. 27(3). 704–721.

13.

Feng, Chaolu, et al.. (2022). GSEnet: feature extraction of gene expression data and its application to Leukemia classification. Mathematical Biosciences & Engineering. 19(5). 4881–4891. 2 indexed citations

14.

Huang, Yan, Jinzhu Yang, Qi Sun, et al.. (2022). Vessel filtering and segmentation of coronary CT angiographic images. International Journal of Computer Assisted Radiology and Surgery. 17(10). 1879–1890. 3 indexed citations

15.

Yang, Jinzhu, et al.. (2020). Frangi based multi-scale level sets for retinal vascular segmentation. Computer Methods and Programs in Biomedicine. 197. 105752–105752. 20 indexed citations

16.

Li, Wei, et al.. (2019). MISS-D: A fast and scalable framework of medical image storage service based on distributed file system. Computer Methods and Programs in Biomedicine. 186. 105189–105189. 14 indexed citations

17.

Li, Wei, et al.. (2019). A Two-level Cloud Storage System Based on Asynchronous Message for Medical Image Big Data. 54–58. 3 indexed citations

18.

Feng, Chaolu, Shaoxiang Zhang, Dazhe Zhao, & Chunming Li. (2016). Simultaneous extraction of endocardial and epicardial contours of the left ventricle by distance regularized level sets. Medical Physics. 43(6Part1). 2741–2755. 28 indexed citations

19.

Feng, Chaolu, Chunming Li, Dazhe Zhao, Christos Davatzikos, & Harold Litt. (2013). Segmentation of the Left Ventricle Using Distance Regularized Two-Layer Level Set Approach. Lecture notes in computer science. 16(Pt 1). 477–484. 30 indexed citations

20.

Feng, Chaolu, et al.. (2012). A CUDA-based reverse gridding algorithm for MR reconstruction. Magnetic Resonance Imaging. 31(2). 313–323. 15 indexed citations

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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