Weiwei Zong

1.2k total citations · 1 hit paper
17 papers, 906 citations indexed

About

Weiwei Zong is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Weiwei Zong has authored 17 papers receiving a total of 906 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 6 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Weiwei Zong's work include Machine Learning and ELM (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Prostate Cancer Diagnosis and Treatment (4 papers). Weiwei Zong is often cited by papers focused on Machine Learning and ELM (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Prostate Cancer Diagnosis and Treatment (4 papers). Weiwei Zong collaborates with scholars based in United States, China and Singapore. Weiwei Zong's co-authors include Guang-Bin Huang, Yiqiang Chen, Weimin Huang, Ning Li, Zhiping Lin, Yuping Duan, Jiayin Zhou, Ning Wen, Milan Pantelic and Mohamed A. Elshaikh and has published in prestigious journals such as Cancer Research, Scientific Reports and International Journal of Radiation Oncology*Biology*Physics.

In The Last Decade

Weiwei Zong

17 papers receiving 875 citations

Hit Papers

Weighted extreme learning machine for imbalance learning 2012 2026 2016 2021 2012 100 200 300 400 500
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.

  1. Weighted extreme learning machine for imbalance learning
    2012 568 citations Weiwei Zong, Guang-Bin Huang et al. Neurocomputing profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Weiwei Zong United States 7 726 244 168 79 62 17 906
Musatafa Abbas Abbood Albadr Malaysia 17 508 0.7× 167 0.7× 75 0.4× 57 0.7× 125 2.0× 34 844
Fahad Taha AL‐Dhief Malaysia 21 772 1.1× 233 1.0× 176 1.0× 68 0.9× 184 3.0× 52 1.4k
Jiao Hu China 8 364 0.5× 97 0.4× 69 0.4× 48 0.6× 76 1.2× 14 643
Siew Chin Neoh Malaysia 15 553 0.8× 478 2.0× 82 0.5× 55 0.7× 68 1.1× 33 1.2k
Feng Yang China 15 253 0.3× 249 1.0× 87 0.5× 19 0.2× 73 1.2× 87 796
Ziliang Chen China 10 485 0.7× 484 2.0× 42 0.3× 34 0.4× 61 1.0× 36 842
Sibo Qiao China 14 145 0.2× 117 0.5× 60 0.4× 60 0.8× 61 1.0× 85 603
Yonggwan Won South Korea 13 324 0.4× 140 0.6× 114 0.7× 67 0.8× 13 0.2× 81 713
Khamron Sunat Thailand 15 466 0.6× 203 0.8× 147 0.9× 63 0.8× 20 0.3× 73 764

Countries citing papers authored by Weiwei Zong

Since Specialization
Citations

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

Fields of papers citing papers by Weiwei Zong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Weiwei Zong

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

All Works

17 of 17 papers shown
1.
Alafif, Tarik, et al.. (2023). Adaptive Batch Normalization for Training Data with Heterogeneous Features. 1–6. 3 indexed citations
2.
Zong, Weiwei, Simeng Zhu, Joon Lee, et al.. (2022). Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization. Scientific Reports. 12(1). 22430–22430. 6 indexed citations
3.
Zong, Xiangyun, Yang Yu, Wenhu Chen, et al.. (2022). Ovarian reserve in premenopausal women with breast cancer. The Breast. 64. 143–150. 4 indexed citations
4.
Dai, Z., Chang Liu, Joon Lee, et al.. (2020). Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic Magnetic Resonance Imaging Using Mask Region-Based Convolutional Neural Networks. Advances in Radiation Oncology. 5(3). 473–481. 36 indexed citations
5.
Zong, Weiwei, Joon Lee, Milan Pantelic, & Ning Wen. (2020). Prediction of Gleason Grade Group of Prostate Cancer on Multiparametric MRI using Deep Machine Learning Models. International Journal of Radiation Oncology*Biology*Physics. 108(2). E9–E10. 1 indexed citations
6.
Zong, Weiwei, Chang Liu, Branislava Janic, et al.. (2020). A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network. Medical Physics. 47(9). 4077–4086. 15 indexed citations
7.
Zong, Weiwei, et al.. (2019). Overall Survival Prediction in Glioblastoma Using Clinical Factors Combined with Texture Features Extracted from 3D Convolutional Neural Networks. International Journal of Radiation Oncology*Biology*Physics. 103(5). E49–E49. 1 indexed citations
8.
Zong, Weiwei, Joon Lee, Chang Liu, James M. Snyder, & Ning Wen. (2019). Abstract 3351: Overall survival prediction of glioblastoma patients combining clinical factors with texture features extracted from 3-D convolutional neural networks. Cancer Research. 79(13_Supplement). 3351–3351. 2 indexed citations
9.
Zong, Weiwei, et al.. (2018). Prostate Lesion Malignancy Classification from Multiparametric MRI Images Using Convolution Neural Network. International Journal of Radiation Oncology*Biology*Physics. 102(3). e373–e373. 1 indexed citations
10.
Lan, Yuan, Xiaohong Han, Weiwei Zong, et al.. (2017). Two-step fault diagnosis framework for rolling element bearings with imbalanced data based on GSA-WELM and GSA-ELM. Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science. 232(16). 2937–2947. 9 indexed citations
11.
Lan, Yuan, et al.. (2016). A two-step fault diagnosis framework for rolling element bearings with imbalanced data. 2. 620–625. 2 indexed citations
12.
Zong, Weiwei & Guang-Bin Huang. (2013). Learning to Rank with Extreme Learning Machine. Neural Processing Letters. 39(2). 155–166. 31 indexed citations
13.
14.
Huang, Weimin, Ning Li, Zhiping Lin, et al.. (2013). Liver tumor detection and segmentation using kernel-based extreme learning machine. PubMed. 2013. 3662–3665. 61 indexed citations
15.
Zong, Weiwei, Guang-Bin Huang, & Yiqiang Chen. (2012). Weighted extreme learning machine for imbalance learning. Neurocomputing. 101. 229–242. 568 indexed citations breakdown →
16.
Zong, Weiwei, Yuan Lan, & Guang-Bin Huang. (2012). ONLINE SEQUENTIAL LEARNING BASED ON ENHANCED EXTREME LEARNING MACHINE USING LEFT OR RIGHT PSEUDO-INVERSE. 300–305. 1 indexed citations
17.
Zong, Weiwei & Guang-Bin Huang. (2011). Face recognition based on extreme learning machine. Neurocomputing. 74(16). 2541–2551. 161 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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