Pengchen Liang

486 total citations · 1 hit paper
28 papers, 225 citations indexed

About

Pengchen Liang is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Pengchen Liang has authored 28 papers receiving a total of 225 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 8 papers in Molecular Biology and 7 papers in Artificial Intelligence. Recurrent topics in Pengchen Liang's work include AI in cancer detection (5 papers), Advanced Neural Network Applications (5 papers) and COVID-19 diagnosis using AI (4 papers). Pengchen Liang is often cited by papers focused on AI in cancer detection (5 papers), Advanced Neural Network Applications (5 papers) and COVID-19 diagnosis using AI (4 papers). Pengchen Liang collaborates with scholars based in China, Hong Kong and Singapore. Pengchen Liang's co-authors include Qing Chang, Renkai Wu, Yinghao Liu, Xuan Huang, Haiqin Zhu, Yuandong Gu, Dongyu Liang, Xiaoxu Cui, Jianguo Chen and Lei Yao and has published in prestigious journals such as Scientific Reports, BMC Bioinformatics and BMC Public Health.

In The Last Decade

Pengchen Liang

24 papers receiving 221 citations

Hit Papers

H-vmunet: High-order Vision Mamba UNet for medical image ... 2025 2026 2025 10 20 30 40
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. H-vmunet: High-order Vision Mamba UNet for medical image segmentation
    2025 46 citations Renkai Wu, Yinghao Liu et al. profile →

Peers

Pengchen Liang
Andreea-Iuliana Ionescu Romania
Zhigang Fu China
Shucheng Cao China
Ruchi Singla India
Zixi Jiang China
Yuguang Ye China
Chenchen Qin China
Yiqing Shi China
Yibo Jiang China
Andreea-Iuliana Ionescu Romania
Pengchen Liang
Citations per year, relative to Pengchen Liang Pengchen Liang (= 1×) peers Andreea-Iuliana Ionescu

Countries citing papers authored by Pengchen Liang

Since Specialization
Citations

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

Fields of papers citing papers by Pengchen Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pengchen Liang

This figure shows the co-authorship network connecting the top 25 collaborators of Pengchen Liang. A scholar is included among the top collaborators of Pengchen Liang 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 Pengchen Liang. Pengchen Liang 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.
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.
Liang, Pengchen, et al.. (2025). WKPNet: A novel wavelet-KAN-POLA network for medical image segmentation. Biomedical Signal Processing and Control. 113. 108988–108988.
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.
5.
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.
6.
Liang, Pengchen, Jianguo Chen, Qing Chang, & Lei Yao. (2024). RSKD: Enhanced medical image segmentation via multi-layer, rank-sensitive knowledge distillation in Vision Transformer models. Knowledge-Based Systems. 293. 111664–111664. 10 indexed citations
7.
Liang, Pengchen, et al.. (2024). DSCU-Net: MEMS Defect Detection Using Dense Skip-Connection U-Net. Symmetry. 16(3). 300–300. 2 indexed citations
8.
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
9.
Chen, Zhuangzhuang, et al.. (2024). Divide and augment: Supervised domain adaptation via sample-wise feature fusion. Information Fusion. 115. 102757–102757. 5 indexed citations
10.
Liang, Pengchen, et al.. (2024). Data free knowledge distillation with feature synthesis and spatial consistency for image analysis. Scientific Reports. 14(1). 27557–27557. 1 indexed citations
11.
Liang, Pengchen, Jianguo Chen, Lei Yao, Zezhou Hao, & Qing Chang. (2023). A Deep Learning Approach for Prognostic Evaluation of Lung Adenocarcinoma Based on Cuproptosis-Related Genes. Biomedicines. 11(5). 1479–1479. 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.
Liang, Pengchen, et al.. (2023). DAWTran: dynamic adaptive windowing transformer network for pneumothorax segmentation with implicit feature alignment. Physics in Medicine and Biology. 68(17). 175020–175020.
14.
Liu, Yixin, Yuan Shao, Zezhou Hao, et al.. (2023). Cuproptosis gene‐related, neural network‐based prognosis prediction and drug‐target prediction for KIRC. Cancer Medicine. 13(1). e6763–e6763. 4 indexed citations
15.
Xu, Li, et al.. (2023). Luteolin inhibits A549 cells proliferation and migration by down-regulating androgen receptors. European journal of medical research. 28(1). 353–353. 10 indexed citations
16.
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
17.
Liu, Yinghao, et al.. (2023). Automatic and efficient pneumothorax segmentation from CT images using EFA-Net with feature alignment function. Scientific Reports. 13(1). 15291–15291. 3 indexed citations
18.
Hao, Zezhou, Pengchen Liang, Changyu He, et al.. (2022). Prognostic risk assessment model and drug sensitivity analysis of colon adenocarcinoma (COAD) based on immune-related lncRNA pairs. BMC Bioinformatics. 23(1). 435–435. 2 indexed citations
19.
Liang, Pengchen, Jin Li, Jianguo Chen, et al.. (2022). Immunoprognostic model of lung adenocarcinoma and screening of sensitive drugs. Scientific Reports. 12(1). 7162–7162. 5 indexed citations
20.
Liang, Pengchen, et al.. (2022). Improvement of polydopamine-loaded salidroside on osseointegration of titanium implants. Chinese Medicine. 17(1). 26–26. 11 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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