Tianhe Wu

609 total citations · 1 hit paper
7 papers, 279 citations indexed

About

Tianhe Wu is a scholar working on Computer Vision and Pattern Recognition, Biomaterials and Media Technology. According to data from OpenAlex, Tianhe Wu has authored 7 papers receiving a total of 279 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Computer Vision and Pattern Recognition, 2 papers in Biomaterials and 2 papers in Media Technology. Recurrent topics in Tianhe Wu's work include Nanoparticle-Based Drug Delivery (2 papers), Advanced Image Fusion Techniques (2 papers) and Image and Video Quality Assessment (2 papers). Tianhe Wu is often cited by papers focused on Nanoparticle-Based Drug Delivery (2 papers), Advanced Image Fusion Techniques (2 papers) and Image and Video Quality Assessment (2 papers). Tianhe Wu collaborates with scholars based in United States, China and United Kingdom. Tianhe Wu's co-authors include Shuwei Shi, Shanshan Lao, Yujiu Yang, Yuan Gong, Jiahao Wang, Mingdeng Cao, Weihao Xia, Hui Mao, Yuancheng Li and Candace C. Fleischer and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nanoscale and Acta Biomaterialia.

In The Last Decade

Tianhe Wu

6 papers receiving 274 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.

MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment

2022 197 citations Tianhe Wu, Shuwei Shi et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tianhe Wu United States	4	191	107	21	18	17	7	279
Jiahao Wang China	4	159 0.8×	81 0.8×	15 0.7×	12 0.7×	16 0.9×	11	227
Shaolin Su China	5	446 2.3×	236 2.2×	15 0.7×	16 0.9×	16 0.9×	12	518
Domonkos Varga Hungary	12	358 1.9×	155 1.4×	24 1.1×	10 0.6×	11 0.6×	37	416
Golam Dastegir Al-Quaderi Bangladesh	4	210 1.1×	94 0.9×	37 1.8×	34 1.9×	24 1.4×	7	304
Pavan C. Madhusudana United States	7	228 1.2×	87 0.8×	26 1.2×	7 0.4×	20 1.2×	9	294
Saba Dadsetan United States	4	190 1.0×	127 1.2×	15 0.7×	29 1.6×	8 0.5×	6	260
S. Alireza Golestaneh United States	6	432 2.3×	287 2.7×	18 0.9×	15 0.8×	10 0.6×	10	483
Praful Gupta United States	8	418 2.2×	193 1.8×	19 0.9×	19 1.1×	12 0.7×	15	469
Magudeeswaran Veluchamy India	13	391 2.0×	233 2.2×	21 1.0×	42 2.3×	8 0.5×	24	465
Yingxue Pang China	3	152 0.8×	44 0.4×	20 1.0×	32 1.8×	6 0.4×	8	243

Countries citing papers authored by Tianhe Wu

Since Specialization

Citations

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

Fields of papers citing papers by Tianhe Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tianhe Wu

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

All Works

Sort: Min cites: Since: Top N: Style:

7 of 7 papers shown

1.

Li, Yuancheng, Zhaobin Zhang, Tianhe Wu, et al.. (2025). A subtype specific probe for targeted magnetic resonance imaging of M2 tumor-associated macrophages in brain tumors. Acta Biomaterialia. 194. 336–351. 3 indexed citations

2.

Wu, Tianhe, et al.. (2025). Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumption. 12742–12752.

3.

Wu, Tianhe, et al.. (2024). Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI). Quantitative Imaging in Medicine and Surgery. 14(4). 2774–2787. 1 indexed citations

4.

Ma, Hedi, et al.. (2024). Shape-dependent cellular uptake of iron oxide nanorods: mechanisms of endocytosis and implications on cell labeling and cellular delivery. Nanoscale. 16(46). 21398–21415. 11 indexed citations

5.

Wu, Tianhe, Claire Liu, Candace C. Fleischer, et al.. (2023). Label-Free Chemically and Molecularly Selective Magnetic Resonance Imaging. SHILAP Revista de lepidopterología. 1(2). 121–139. 6 indexed citations

6.

Wu, Tianhe, Shuwei Shi, Shanshan Lao, et al.. (2022). MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 1190–1199. 197 indexed citations breakdown →

7.

Lao, Shanshan, Yuan Gong, Shuwei Shi, et al.. (2022). Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 1139–1148. 61 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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