Baisen Cong

893 total citations · 1 hit paper
8 papers, 640 citations indexed

About

Baisen Cong is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Baisen Cong has authored 8 papers receiving a total of 640 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 4 papers in Computer Vision and Pattern Recognition and 4 papers in Computational Theory and Mathematics. Recurrent topics in Baisen Cong's work include Advanced Neural Network Applications (4 papers), Computational Drug Discovery Methods (4 papers) and Protein Structure and Dynamics (3 papers). Baisen Cong is often cited by papers focused on Advanced Neural Network Applications (4 papers), Computational Drug Discovery Methods (4 papers) and Protein Structure and Dynamics (3 papers). Baisen Cong collaborates with scholars based in China, United States and Canada. Baisen Cong's co-authors include Zhiqin Zhu, Guanqiu Qi, Yuanyuan Li, Yü Liu, Xinbo Gao, Neal Mazur, Yuanyuan Li, Litao Bai, Yifei Gong and Xin Zheng and has published in prestigious journals such as Expert Systems with Applications, Information Fusion and Engineering Applications of Artificial Intelligence.

In The Last Decade

Baisen Cong

8 papers receiving 627 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.

Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI

2022 376 citations Zhiqin Zhu, Guanqiu Qi et al. Information Fusion profile →

Peers

Baisen Cong

CVPR

NEURO

CTM

AI

MB

Peiman Keshavarzian Iran

Jinyong Cheng China

Yongxian Fan China

Liangliang Liu China

Jiazhou Chen China

Hamid Zouaki Morocco

Alireza Norouzi Malaysia

Hiba Chougrad Morocco

Najah Alsubaie Saudi Arabia

Peiman Keshavarzian Iran

Baisen Cong

133 ×0.4

CVPR

79 ×0.4

NEURO

85 ×0.6

CTM

234 ×1.6

AI

29 ×0.2

MB

Citations per year, relative to Baisen Cong Baisen Cong (= 1×) peers Peiman Keshavarzian

Countries citing papers authored by Baisen Cong

Since Specialization

Citations

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

Fields of papers citing papers by Baisen Cong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Baisen Cong

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

8 of 8 papers shown

1.

Zhu, Zhiqin, Yan Ding, Guanqiu Qi, et al.. (2025). Drug–target affinity prediction using rotary encoding and information retention mechanisms. Engineering Applications of Artificial Intelligence. 147. 110239–110239. 12 indexed citations

2.

Li, Yuanyuan, et al.. (2025). A Lightweight Vision Mamba Coding UNet for medical image segmentation. Engineering Applications of Artificial Intelligence. 162. 112676–112676. 1 indexed citations

3.

Zhu, Zhiqin, Guanqiu Qi, Baisen Cong, et al.. (2024). Lightweight medical image segmentation network with multi-scale feature-guided fusion. Computers in Biology and Medicine. 182. 109204–109204. 39 indexed citations

4.

Zhu, Zhiqin, Xin Zheng, Guanqiu Qi, et al.. (2024). Drug–target binding affinity prediction model based on multi-scale diffusion and interactive learning. Expert Systems with Applications. 255. 124647–124647. 40 indexed citations

5.

Qi, Guanqiu, et al.. (2023). Medical image segmentation method based on multi-feature interaction and fusion over cloud computing. Simulation Modelling Practice and Theory. 126. 102769–102769. 67 indexed citations

6.

Zhu, Zhiqin, Xin Zheng, Guanqiu Qi, et al.. (2023). Drug–target affinity prediction method based on multi-scale information interaction and graph optimization. Computers in Biology and Medicine. 167. 107621–107621. 57 indexed citations

7.

Zhu, Zhiqin, et al.. (2023). Associative learning mechanism for drug‐target interaction prediction. CAAI Transactions on Intelligence Technology. 8(4). 1558–1577. 48 indexed citations

8.

Zhu, Zhiqin, et al.. (2022). Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI. Information Fusion. 91. 376–387. 376 indexed citations breakdown →

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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