Junlong Cheng

834 total citations
22 papers, 413 citations indexed

About

Junlong Cheng is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Junlong Cheng has authored 22 papers receiving a total of 413 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 13 papers in Radiology, Nuclear Medicine and Imaging and 11 papers in Artificial Intelligence. Recurrent topics in Junlong Cheng's work include Advanced Neural Network Applications (10 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and AI in cancer detection (9 papers). Junlong Cheng is often cited by papers focused on Advanced Neural Network Applications (10 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and AI in cancer detection (9 papers). Junlong Cheng collaborates with scholars based in China, United Kingdom and Australia. Junlong Cheng's co-authors include Shengwei Tian, Hongchun Lu, Chengrui Gao, Long Yu, Xiang Ma, Shijia Liu, Xiaojing Kang, Weidong Wu, Long Yu and Xiaoyi Lv and has published in prestigious journals such as Expert Systems with Applications, IEEE Transactions on Vehicular Technology and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Junlong Cheng

22 papers receiving 406 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Junlong Cheng China 8 237 165 128 80 62 22 413
Dominik Müller Germany 6 141 0.6× 173 1.0× 270 2.1× 80 1.0× 63 1.0× 16 540
D. R. Sarvamangala India 3 133 0.6× 194 1.2× 207 1.6× 70 0.9× 87 1.4× 5 541
Yinghao Zhang China 6 231 1.0× 141 0.9× 150 1.2× 68 0.8× 102 1.6× 27 453
Hoel Kervadec Canada 7 300 1.3× 196 1.2× 232 1.8× 86 1.1× 42 0.7× 12 541
Mahboubeh Jannesari Germany 3 108 0.5× 234 1.4× 245 1.9× 79 1.0× 55 0.9× 7 510
Qingyue Wei China 5 148 0.6× 144 0.9× 128 1.0× 51 0.6× 71 1.1× 11 404
Jichen Yang United States 6 145 0.6× 107 0.6× 155 1.2× 70 0.9× 37 0.6× 19 481
Shuyue Guan United States 11 232 1.0× 261 1.6× 263 2.1× 61 0.8× 54 0.9× 27 556

Countries citing papers authored by Junlong Cheng

Since Specialization
Citations

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

Fields of papers citing papers by Junlong Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junlong Cheng

This figure shows the co-authorship network connecting the top 25 collaborators of Junlong Cheng. A scholar is included among the top collaborators of Junlong Cheng 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 Junlong Cheng. Junlong Cheng 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.
2.
Deng, Zhongying, Junlong Cheng, Yanzhou Su, et al.. (2025). SAM-Med3D: A Vision Foundation Model for General-Purpose Segmentation on Volumetric Medical Images. IEEE Transactions on Neural Networks and Learning Systems. 36(10). 17599–17612. 1 indexed citations
3.
Yu, Jiong, et al.. (2025). Semantic Preservation-Based Hash Code Generation for fine-grained image retrieval. Expert Systems with Applications. 271. 126668–126668. 1 indexed citations
4.
Deng, Zhongying, Ye Jin, Yanzhou Su, et al.. (2025). A-Eval: A benchmark for cross-dataset and cross-modality evaluation of abdominal multi-organ segmentation. Medical Image Analysis. 101. 103499–103499. 1 indexed citations
5.
Cheng, Junlong, Bin Fu, Jin Ye, et al.. (2025). Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline. 20841–20851. 4 indexed citations
6.
Li, Jingwen, et al.. (2024). SEAformer: Selective Edge Aggregation transformer for 2D medical image segmentation. Biomedical Signal Processing and Control. 102. 107203–107203. 2 indexed citations
7.
Cheng, Junlong, et al.. (2024). PL-Net: progressive learning network for medical image segmentation. Frontiers in Bioengineering and Biotechnology. 12. 1414605–1414605. 1 indexed citations
8.
Yang, Yong, et al.. (2023). LatLRR-CNN: an infrared and visible image fusion method combining latent low-rank representation and CNN. Multimedia Tools and Applications. 82(23). 36303–36323. 7 indexed citations
9.
Gao, Chengrui, et al.. (2023). SAA-NET: A Medical Image Segmentation Framework Based On Stream-Across Attention. 1–5. 1 indexed citations
10.
Lu, Hongchun, et al.. (2023). AMLNet: Attention Multibranch Loss CNN Models for Fine-Grained Vehicle Recognition. IEEE Transactions on Vehicular Technology. 73(1). 375–384. 2 indexed citations
11.
Wang, Yongtao, Shengwei Tian, Long Yu, et al.. (2022). FSOU-Net: Feature supplement and optimization U-Net for 2D medical image segmentation. Technology and Health Care. 31(1). 181–195. 2 indexed citations
12.
Cheng, Junlong, Shengwei Tian, Long Yu, et al.. (2022). DDU-Net: A dual dense U-structure network for medical image segmentation. Applied Soft Computing. 126. 109297–109297. 30 indexed citations
13.
Fan, Xing, Shengwei Tian, Long Yu, et al.. (2022). Calibration and Distraction Mining Network for Aortic True Lumen segmentation. Journal of Intelligent & Fuzzy Systems. 43(6). 7863–7875. 1 indexed citations
14.
Cheng, Junlong, Shengwei Tian, Long Yu, et al.. (2021). ResGANet: Residual group attention network for medical image classification and segmentation. Medical Image Analysis. 76. 102313–102313. 139 indexed citations
15.
Lu, Hongchun, Shengwei Tian, Long Yu, et al.. (2021). DCACNet: Dual context aggregation and attention-guided cross deconvolution network for medical image segmentation. Computer Methods and Programs in Biomedicine. 214. 106566–106566. 11 indexed citations
16.
Wang, Chaoqing, et al.. (2021). DBLCNN: Dependency-based lightweight convolutional neural network for multi-classification of breast histopathology images. Biomedical Signal Processing and Control. 73. 103451–103451. 20 indexed citations
17.
Wang, Chaoqing, et al.. (2021). Hierarchical Scheme for Vehicle Make and Model Recognition. Transportation Research Record Journal of the Transportation Research Board. 2675(7). 363–376. 2 indexed citations
18.
Cheng, Junlong, Shengwei Tian, Long Yu, Hongchun Lu, & Xiaoyi Lv. (2020). Fully convolutional attention network for biomedical image segmentation. Artificial Intelligence in Medicine. 107. 101899–101899. 57 indexed citations
19.
Cheng, Junlong, et al.. (2020). Multi-Attention Mechanism Medical Image Segmentation Combined with Word Embedding Technology. Automatic Control and Computer Sciences. 54(6). 560–571. 4 indexed citations
20.
Cheng, Junlong, Shengwei Tian, Long Yu, Xiang Ma, & Yan Xing. (2020). A deep learning algorithm using contrast-enhanced computed tomography (CT) images for segmentation and rapid automatic detection of aortic dissection. Biomedical Signal Processing and Control. 62. 102145–102145. 27 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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