Manning Wang

7.0k total citations
124 papers, 1.7k citations indexed

About

Manning Wang is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Artificial Intelligence. According to data from OpenAlex, Manning Wang has authored 124 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 63 papers in Computer Vision and Pattern Recognition, 33 papers in Aerospace Engineering and 25 papers in Artificial Intelligence. Recurrent topics in Manning Wang's work include Robotics and Sensor-Based Localization (30 papers), Medical Image Segmentation Techniques (22 papers) and 3D Shape Modeling and Analysis (12 papers). Manning Wang is often cited by papers focused on Robotics and Sensor-Based Localization (30 papers), Medical Image Segmentation Techniques (22 papers) and 3D Shape Modeling and Analysis (12 papers). Manning Wang collaborates with scholars based in China, Germany and Taiwan. Manning Wang's co-authors include Zhijian Song, Shaolei Liu, Linhao Qu, Kexue Fu, Zhijian Song, Yifeng Fan, Weiwei Deng, Dongsheng Jiang, Yinlong Liu and Qiuye Jin and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Manning Wang

112 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Manning Wang China 22 645 378 331 302 266 124 1.7k
Jingfan Fan China 19 833 1.3× 605 1.6× 111 0.3× 365 1.2× 240 0.9× 124 1.5k
Maximilian Baust Germany 14 637 1.0× 499 1.3× 101 0.3× 186 0.6× 743 2.8× 33 1.5k
Chenyang Xu United States 16 1.7k 2.7× 567 1.5× 149 0.5× 629 2.1× 364 1.4× 32 2.9k
F. Pernuš Slovenia 13 813 1.3× 584 1.5× 160 0.5× 511 1.7× 82 0.3× 31 1.5k
Dejan Tomaževič Slovenia 16 752 1.2× 479 1.3× 169 0.5× 537 1.8× 58 0.2× 42 1.5k
Tianyu Xiang China 18 602 0.9× 120 0.3× 209 0.6× 224 0.7× 232 0.9× 103 2.0k
William A. Barrett United States 15 1.2k 1.9× 235 0.6× 92 0.3× 146 0.5× 159 0.6× 49 1.7k
Jun Cheng China 28 1.5k 2.4× 2.1k 5.5× 107 0.3× 478 1.6× 373 1.4× 160 3.3k
Toru Tamaki Japan 17 382 0.6× 290 0.8× 100 0.3× 93 0.3× 236 0.9× 164 1.4k
Miao Liao China 25 908 1.4× 244 0.6× 94 0.3× 134 0.4× 184 0.7× 102 1.6k

Countries citing papers authored by Manning Wang

Since Specialization
Citations

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

Fields of papers citing papers by Manning Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manning Wang

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

All Works

20 of 20 papers shown
1.
Zhao, Jiayue, et al.. (2025). Deep-learning-based 3D multi-view multi-parametric MRI fusion model for preoperative T-staging of rectal cancer. Biomedical Signal Processing and Control. 113. 108787–108787.
2.
Jin, Qiuye, Xiaofei Du, Liu Wang, et al.. (2025). MDAL: Modality-difference-based active learning for multimodal medical image analysis via contrastive learning and pointwise mutual information. Computerized Medical Imaging and Graphics. 123. 102544–102544.
3.
An, Bohan, et al.. (2025). SeLa-MIL: Developing an instance-level classifier via weakly-supervised self-training for whole slide image classification. Computer Methods and Programs in Biomedicine. 261. 108614–108614. 1 indexed citations
4.
Qu, Linhao, et al.. (2024). Rethinking deep active learning for medical image segmentation: A diffusion and angle-based framework. Biomedical Signal Processing and Control. 96. 106493–106493. 2 indexed citations
5.
An, Bohan, et al.. (2024). ProteinF3S: boosting enzyme function prediction by fusing protein sequence, structure, and surface. Briefings in Bioinformatics. 26(1). 1 indexed citations
6.
Wang, Manning, et al.. (2024). Complementary multi-modality molecular self-supervised learning via non-overlapping masking for property prediction. Briefings in Bioinformatics. 25(4). 5 indexed citations
7.
Qu, Linhao, et al.. (2024). Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier Is All You Need. IEEE Transactions on Circuits and Systems for Video Technology. 34(10). 9732–9744. 16 indexed citations
8.
Jin, Qiuye, et al.. (2024). A comprehensive survey on deep active learning in medical image analysis. Medical Image Analysis. 95. 103201–103201. 35 indexed citations
9.
Fu, Kexue, et al.. (2024). Robust Point Cloud Registration via Random Network Co-Ensemble. IEEE Transactions on Circuits and Systems for Video Technology. 34(7). 5742–5752. 2 indexed citations
10.
Huang, Qiao, et al.. (2024). Exploring Self-Supervised Learning for 3D Point Cloud Registration. IEEE Robotics and Automation Letters. 10(1). 25–31.
11.
Wang, Manning, et al.. (2024). PGBind: pocket-guided explicit attention learning for protein–ligand docking. Briefings in Bioinformatics. 25(5). 3 indexed citations
12.
Chen, Kun, Manning Wang, & Zhijian Song. (2023). Multi-task learning-based histologic subtype classification of non-small cell lung cancer. La radiologia medica. 128(5). 537–543. 13 indexed citations
13.
Qu, Linhao, et al.. (2023). Negative Instance Guided Self-Distillation Framework for Whole Slide Image Analysis. IEEE Journal of Biomedical and Health Informatics. 28(2). 964–975. 6 indexed citations
14.
Wang, Manning, et al.. (2023). PI-NeRF: A Partial-Invertible Neural Radiance Fields for Pose Estimation. 7826–7836. 2 indexed citations
15.
Liu, Yinlong, Yiru Wang, Manning Wang, et al.. (2022). Globally Optimal Linear Model Fitting with Unit-Norm Constraint. International Journal of Computer Vision. 130(4). 933–946. 6 indexed citations
16.
Fu, Kexue, et al.. (2022). Dual-Branch Deep Point Cloud Registration Framework for Unconstrained Rotation. IEEE Transactions on Industrial Informatics. 19(7). 7851–7861. 5 indexed citations
17.
Fu, Kexue, et al.. (2022). Robust Point Cloud Registration Framework Based on Deep Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(5). 1–13. 24 indexed citations
18.
Fu, Kexue, Yinlong Liu, & Manning Wang. (2021). Global Registration of 3D Cerebral Vessels to Its 2D Projections by a New Branch-and-Bound Algorithm. IEEE Transactions on Medical Robotics and Bionics. 3(1). 115–124. 5 indexed citations
19.
Liu, Shaolei, Zhijian Song, & Manning Wang. (2020). WaveFuse: A Unified Deep Framework for Image Fusion with Wavelet Transform. arXiv (Cornell University). 4 indexed citations
20.
Liu, Yinlong, et al.. (2018). 2D-3D Point Set Registration Based on Global Rotation Search. IEEE Transactions on Image Processing. 28(5). 2599–2613. 23 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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