Baochun He

1.6k total citations
24 papers, 352 citations indexed

About

Baochun He is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, Baochun He has authored 24 papers receiving a total of 352 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 12 papers in Radiology, Nuclear Medicine and Imaging and 10 papers in Biomedical Engineering. Recurrent topics in Baochun He's work include Radiomics and Machine Learning in Medical Imaging (10 papers), Medical Image Segmentation Techniques (9 papers) and Medical Imaging and Analysis (7 papers). Baochun He is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (10 papers), Medical Image Segmentation Techniques (9 papers) and Medical Imaging and Analysis (7 papers). Baochun He collaborates with scholars based in China, United States and Philippines. Baochun He's co-authors include Fucang Jia, Chihua Fang, Deqiang Xiao, Qingmao Hu, Nan Xiang, Wei Cai, Wenyu Zhang, Cheng Huang, Huoling Luo and Xue Lin and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Medical Physics.

In The Last Decade

Baochun He

23 papers receiving 346 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Baochun He China 11 193 128 103 80 77 24 352
Matthias Thorn Germany 9 115 0.6× 89 0.7× 75 0.7× 77 1.0× 58 0.8× 18 349
Akshat Gotra Canada 10 136 0.7× 87 0.7× 45 0.4× 74 0.9× 25 0.3× 13 324
Ali Barah Qatar 6 79 0.4× 40 0.3× 57 0.6× 42 0.5× 69 0.9× 18 352
Jason Xie Canada 12 405 2.1× 38 0.3× 108 1.0× 45 0.6× 48 0.6× 24 943
Martin Segeroth Switzerland 5 337 1.7× 78 0.6× 180 1.7× 14 0.2× 48 0.6× 11 516
Michael Suehling Germany 14 365 1.9× 212 1.7× 276 2.7× 35 0.4× 33 0.4× 28 727
Stefan Schmidt Germany 8 181 0.9× 102 0.8× 114 1.1× 33 0.4× 48 0.6× 22 472
Junming Jian China 13 527 2.7× 52 0.4× 133 1.3× 100 1.3× 92 1.2× 22 624
Sebastian Eulenstein Germany 10 88 0.5× 135 1.1× 114 1.1× 85 1.1× 40 0.5× 17 328

Countries citing papers authored by Baochun He

Since Specialization
Citations

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

Fields of papers citing papers by Baochun He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Baochun He

This figure shows the co-authorship network connecting the top 25 collaborators of Baochun He. A scholar is included among the top collaborators of Baochun He 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 Baochun He. Baochun He 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.
Jiang, Hao, Baochun He, Yuchong Li, et al.. (2025). A 3D ‐ 2D Rigid Liver Registration Method Using Pre‐Training and Transfer Learning With Staged Alignment of Anatomical Landmarks. International Journal of Imaging Systems and Technology. 35(4).
2.
Li, Yuchong, et al.. (2023). MT-FiST: A Multi-Task Fine-Grained Spatial-Temporal Framework for Surgical Action Triplet Recognition. IEEE Journal of Biomedical and Health Informatics. 27(10). 4983–4994. 8 indexed citations
3.
He, Baochun, Libo Liu, Haoyu Hu, et al.. (2023). UMRFormer-net: a three-dimensional U-shaped pancreas segmentation method based on a double-layer bridged transformer network. Quantitative Imaging in Medicine and Surgery. 13(3). 1619–1630. 10 indexed citations
4.
5.
He, Baochun, Sheng Zhao, Jiaqi Wu, et al.. (2023). A robust and automatic CT‐3D ultrasound registration method based on segmentation, context, and edge hybrid metric. Medical Physics. 50(10). 6243–6258. 2 indexed citations
6.
He, Baochun, et al.. (2022). Diagnostic accuracy of 3D imaging combined with intra-operative ultrasound in the prediction of post-hepatectomy liver failure. Journal of Gastrointestinal Oncology. 13(3). 1224–1236. 2 indexed citations
7.
Huang, Wenhao, et al.. (2022). Acute abdominal disease associated with gallbladder torsion recovered after cholecystectomy: a rare case report and literature review. Annals of Translational Medicine. 10(10). 616–616. 2 indexed citations
8.
Lin, Xue, Sheng Zhao, Huijie Jiang, et al.. (2021). A radiomics-based nomogram for preoperative T staging prediction of rectal cancer. Abdominal Radiology. 46(10). 4525–4535. 20 indexed citations
9.
He, Baochun, Dalong Yin, Xiaoxia Chen, et al.. (2021). A study of generalization and compatibility performance of 3D U-Net segmentation on multiple heterogeneous liver CT datasets. BMC Medical Imaging. 21(1). 178–178. 3 indexed citations
10.
11.
Lin, Xue, et al.. (2020). MDCC-Net: Multiscale double-channel convolution U-Net framework for colorectal tumor segmentation. Computers in Biology and Medicine. 130. 104183–104183. 25 indexed citations
12.
He, Mu, Peng Zhang, Xiao Ma, et al.. (2020). Radiomic Feature-Based Predictive Model for Microvascular Invasion in Patients With Hepatocellular Carcinoma. Frontiers in Oncology. 10. 574228–574228. 24 indexed citations
13.
Luo, Huoling, Dalong Yin, Shugeng Zhang, et al.. (2019). Augmented reality navigation for liver resection with a stereoscopic laparoscope. Computer Methods and Programs in Biomedicine. 187. 105099–105099. 72 indexed citations
14.
Xiao, Deqiang, et al.. (2019). Prediction of Liver Respiratory Motion Based on Machine Learning. 1228–1233. 1 indexed citations
15.
Zhang, Wenyu, Wei Cai, Baochun He, et al.. (2018). A radiomics-based formula for the preoperative prediction of postoperative pancreatic fistula in patients with pancreaticoduodenectomy. Cancer Management and Research. Volume 10. 6469–6478. 31 indexed citations
16.
Cai, Wei, Baochun He, Min Hu, et al.. (2018). A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma. Surgical Oncology. 28. 78–85. 47 indexed citations
17.
He, Baochun, Deqiang Xiao, Qingmao Hu, & Fucang Jia. (2017). Automatic Magnetic Resonance Image Prostate Segmentation Based on Adaptive Feature Learning Probability Boosting Tree Initialization and CNN-ASM Refinement. IEEE Access. 6. 2005–2015. 23 indexed citations
18.
Cai, Wei, Baochun He, Yingfang Fan, Chihua Fang, & Fucang Jia. (2016). Comparison of liver volumetry on contrast‐enhanced CT images: one semiautomatic and two automatic approaches. Journal of Applied Clinical Medical Physics. 17(6). 118–127. 9 indexed citations
19.
He, Baochun, Cheng Huang, G Sharp, et al.. (2016). Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model. Medical Physics. 43(5). 2421–2434. 25 indexed citations
20.
Jiang, Huiyan, et al.. (2013). A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information. Computational and Mathematical Methods in Medicine. 2013. 1–9. 15 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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