Renkai Wu

452 total citations · 2 hit papers
15 papers, 165 citations indexed

About

Renkai Wu is a scholar working on Computer Vision and Pattern Recognition, Oncology and Artificial Intelligence. According to data from OpenAlex, Renkai Wu has authored 15 papers receiving a total of 165 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 6 papers in Oncology and 4 papers in Artificial Intelligence. Recurrent topics in Renkai Wu's work include Medical Image Segmentation Techniques (4 papers), AI in cancer detection (4 papers) and Cutaneous Melanoma Detection and Management (4 papers). Renkai Wu is often cited by papers focused on Medical Image Segmentation Techniques (4 papers), AI in cancer detection (4 papers) and Cutaneous Melanoma Detection and Management (4 papers). Renkai Wu collaborates with scholars based in China, United Kingdom and United States. Renkai Wu's co-authors include Qing Chang, Pengchen Liang, Yinghao Liu, Xuan Huang, Haiqin Zhu, Yuandong Gu, Guochen Ning, Xiaoxu Cui, Yiqi Huang and Wei Fang and has published in prestigious journals such as Scientific Reports, International Journal of Radiation Oncology*Biology*Physics and BMC Bioinformatics.

In The Last Decade

Renkai Wu

11 papers receiving 163 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.

H-vmunet: High-order Vision Mamba UNet for medical image segmentation

2025 46 citations Renkai Wu, Yinghao Liu et al. Neurocomputing profile →
UltraLight VM-UNet: Parallel Vision Mamba significantly reduces parameters for skin lesion segmentation

2025 30 citations Renkai Wu, Yinghao Liu et al. Patterns profile →

Peers

Renkai Wu

CVPR

AI

ONCOL

RNMI

NEURO

Sahadev Poudel South Korea

Yihong Qiao China

Aimon Rahman Bangladesh

Redha Ali United States

Pranjal Sahu United States

Mustafa Munir United States

Xinrui Zhou China

Chengrui Gao China

Yusuke Kurose Japan

Hongchun Lu China

Sahadev Poudel South Korea

Renkai Wu

84 ×1.0

CVPR

101 ×1.6

AI

47 ×0.9

ONCOL

62 ×2.0

RNMI

17 ×0.8

NEURO

Citations per year, relative to Renkai Wu Renkai Wu (= 1×) peers Sahadev Poudel

Countries citing papers authored by Renkai Wu

Since Specialization

Citations

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

Fields of papers citing papers by Renkai Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Renkai Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Renkai Wu. A scholar is included among the top collaborators of Renkai 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 Renkai Wu. Renkai 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:

15 of 15 papers shown

1.

Wu, Renkai, Lin Pan, Pengchen Liang, et al.. (2025). SK-VM++: Mamba assists skip-connections for medical image segmentation. Biomedical Signal Processing and Control. 105. 107646–107646. 6 indexed citations

2.

Shi, Lei, Renkai Wu, Jianguo Chen, et al.. (2025). MambaSAM: A Visual Mamba-Adapted SAM Framework for Medical Image Segmentation. IEEE Journal of Biomedical and Health Informatics. 29(8). 5824–5835.

3.

Cui, Xiaoxu, Renkai Wu, Yinghao Liu, et al.. (2025). scSMD: a deep learning method for accurate clustering of single cells based on auto-encoder. BMC Bioinformatics. 26(1). 33–33. 1 indexed citations

4.

Wu, Renkai, et al.. (2025). UltraLight VM-UNet: Parallel Vision Mamba significantly reduces parameters for skin lesion segmentation. Patterns. 6(11). 101298–101298. 30 indexed citations breakdown →

5.

Wu, Renkai, Yinghao Liu, Pengchen Liang, & Qing Chang. (2025). H-vmunet: High-order Vision Mamba UNet for medical image segmentation. Neurocomputing. 624. 129447–129447. 46 indexed citations breakdown →

6.

Wu, Renkai, et al.. (2025). Toward Zero-Shot Learning for Visual Dehazing of Urological Surgical Robots. 4070–4076. 1 indexed citations

7.

Wu, Renkai, et al.. (2024). MCF-SMSIS: Multi-tasking with complementary functions for stereo matching and surgical instrument segmentation. Computers in Biology and Medicine. 179. 108923–108923.

8.

Huang, Xinping, Kui Wang, Renkai Wu, et al.. (2024). Neoadjuvant Chemotherapy plus Tislelizumab Followed by Adjuvant Tislelizumab for Locoregionally Advanced Nasopharyngeal Carcinoma: A Single-Arm, Phase II Trial. International Journal of Radiation Oncology*Biology*Physics. 120(2). e791–e791. 1 indexed citations

9.

Jiang, Chenghao, Renkai Wu, Yinghao Liu, et al.. (2024). A high-order focus interaction model and oral ulcer dataset for oral ulcer segmentation. Scientific Reports. 14(1). 20085–20085. 1 indexed citations

10.

Wu, Renkai, et al.. (2024). Laparoscopic stereo matching using 3-Dimensional Fourier transform with full multi-scale features. Engineering Applications of Artificial Intelligence. 139. 109654–109654.

11.

Wu, Renkai, et al.. (2024). Automatic Segmentation of Hemorrhages in the Ultra-Wide Field Retina: Multi-Scale Attention Subtraction Networks and an Ultra-Wide Field Retinal Hemorrhage Dataset. IEEE Journal of Biomedical and Health Informatics. 28(12). 7369–7381. 5 indexed citations

12.

Wu, Renkai, Pengchen Liang, Xuan Huang, et al.. (2023). MHorUNet: High-order spatial interaction UNet for skin lesion segmentation. Biomedical Signal Processing and Control. 88. 105517–105517. 48 indexed citations

13.

Wu, Renkai, et al.. (2023). HSH-UNet: Hybrid selective high order interactive U-shaped model for automated skin lesion segmentation. Computers in Biology and Medicine. 168. 107798–107798. 24 indexed citations

14.

Wu, Renkai, Pengchen Liang, Xuan Huang, et al.. (2023). Automatic Skin Lesion Segmentation Based on Higher-Order Spatial Interaction Model. 447–452.

15.

Liu, Qiang, et al.. (2022). Lymph Node Metastasis Spread Patterns and the Effectiveness of Prophylactic Neck Irradiation in Sinonasal Squamous Cell Carcinoma (SNSCC). International Journal of Radiation Oncology*Biology*Physics. 114(3). e302–e302. 2 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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