Jun Shi

2.0k total citations
114 papers, 1.2k citations indexed

About

Jun Shi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Jun Shi has authored 114 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Computer Vision and Pattern Recognition, 47 papers in Artificial Intelligence and 14 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Jun Shi's work include AI in cancer detection (29 papers), Advanced Image and Video Retrieval Techniques (15 papers) and Image Retrieval and Classification Techniques (14 papers). Jun Shi is often cited by papers focused on AI in cancer detection (29 papers), Advanced Image and Video Retrieval Techniques (15 papers) and Image Retrieval and Classification Techniques (14 papers). Jun Shi collaborates with scholars based in China, United States and Hong Kong. Jun Shi's co-authors include Zhiguo Jiang, Fengying Xie, Yushan Zheng, Hao Feng, Ping Yang, Long Chen, Haopeng Zhang, Ruoyu Wang, Chenghai Xue and Yue Gao and has published in prestigious journals such as Oncogene, Journal of Colloid and Interface Science and IEEE Transactions on Medical Imaging.

In The Last Decade

Jun Shi

97 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jun Shi China	17	526	413	218	197	158	114	1.2k
Muhammad Khusairi Osman Malaysia	18	253 0.5×	323 0.8×	189 0.9×	149 0.8×	132 0.8×	144	987
Rufeng Zhang China	11	1.2k 2.4×	528 1.3×	226 1.0×	106 0.5×	74 0.5×	14	1.7k
Yang Zhang China	21	695 1.3×	534 1.3×	120 0.6×	100 0.5×	451 2.9×	126	2.0k
Sheng Huang China	24	574 1.1×	403 1.0×	123 0.6×	319 1.6×	153 1.0×	165	2.0k
Yi Zhu China	19	1.3k 2.5×	797 1.9×	234 1.1×	133 0.7×	57 0.4×	52	2.1k
Qing Liu China	20	481 0.9×	303 0.7×	148 0.7×	348 1.8×	27 0.2×	121	1.4k
Fanhua Yu China	21	366 0.7×	675 1.6×	112 0.5×	202 1.0×	124 0.8×	47	1.7k
Sankhadeep Chatterjee India	20	337 0.6×	338 0.8×	68 0.3×	98 0.5×	88 0.6×	75	1.2k
Sang-Woong Lee South Korea	19	432 0.8×	371 0.9×	183 0.8×	157 0.8×	87 0.6×	92	1.4k
Pan Zhou China	18	1.1k 2.1×	599 1.5×	296 1.4×	162 0.8×	32 0.2×	62	2.2k

Countries citing papers authored by Jun Shi

Since Specialization

Citations

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

Fields of papers citing papers by Jun Shi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Shi

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

20 of 20 papers shown

Ying, Shihui, et al.. (2025). Dual-Domain Spatial-Temporal reconstruction network for reconstruction of cine CMR. Biomedical Signal Processing and Control. 107. 107836–107836.

Jiang, Zhiguo, et al.. (2025). Pathology report generation from whole slide images with knowledge retrieval and multi-level regional feature selection. Computer Methods and Programs in Biomedicine. 263. 108677–108677.

Shi, Jun, et al.. (2025). Development of downscaling technology for land surface temperature: A case study of Shanghai, China. Urban Climate. 61. 102412–102412. 1 indexed citations

Kong, Xue, Jun Shi, Dongdong Sun, et al.. (2025). A deep‐learning model for predicting tyrosine kinase inhibitor response from histology in gastrointestinal stromal tumor. The Journal of Pathology. 265(4). 462–471.

Shi, Jun, Dongdong Sun, Zhiguo Jiang, et al.. (2024). Positional encoding-guided transformer-based multiple instance learning for histopathology whole slide images classification. Computer Methods and Programs in Biomedicine. 258. 108491–108491. 6 indexed citations

Shi, Jun, Zhiguo Jiang, Liping Zheng, et al.. (2024). Masked hypergraph learning for weakly supervised histopathology whole slide image classification. Computer Methods and Programs in Biomedicine. 253. 108237–108237. 9 indexed citations

Jiang, Zhiguo, Jun Shi, Fengying Xie, et al.. (2024). Pan-Cancer Histopathology WSI Pre-Training With Position-Aware Masked Autoencoder. IEEE Transactions on Medical Imaging. 44(4). 1610–1623. 2 indexed citations

Cao, Weifeng, et al.. (2024). HASN: hybrid attention separable network for efficient image super-resolution. The Visual Computer. 41(5). 3423–3435. 2 indexed citations

Jiang, Zhiguo, Jun Shi, Fengying Xie, et al.. (2024). Self-Supervised Representation Distribution Learning for Reliable Data Augmentation in Histopathology WSI Classification. IEEE Transactions on Medical Imaging. 44(1). 462–474. 4 indexed citations

10.

Zheng, Yushan, Kun Wu, Jun Li, et al.. (2024). Partial-Label Contrastive Representation Learning for Fine-Grained Biomarkers Prediction From Histopathology Whole Slide Images. IEEE Journal of Biomedical and Health Informatics. 29(1). 396–408. 2 indexed citations

11.

Qu, Z. H., et al.. (2023). Synthetic aperture radar ground target image generation based on improved Wasserstein generative adversarial networks with gradient penalty. Journal of Applied Remote Sensing. 17(3). 1 indexed citations

12.

Ai, Jiaqiu, et al.. (2021). SAR image speckle noise suppression algorithm based on background homogeneity and bilateral filtering. National Remote Sensing Bulletin. 25(5). 1071–1084.

13.

Zheng, Yushan, Zhiguo Jiang, Jun Shi, et al.. (2021). Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval. Medical Image Analysis. 76. 102308–102308. 16 indexed citations

14.

Xu, Jin, et al.. (2020). Survey on the applications of deep learning to histopathology. Journal of Image and Graphics. 25(10). 1982–1993. 4 indexed citations

15.

Li, Zhou, et al.. (2017). ジクロフェナク分解のための強化された可視光光触媒活性を有する新規可視光駆動AgI/g C_3N_4複合材料の作製【Powered by NICT】. Journal of Colloid and Interface Science. 496. 176. 1 indexed citations

16.

Shi, Jun, et al.. (2012). A fast sparse representation based classification. 48(1). 70–76. 2 indexed citations

17.

Shi, Jun, et al.. (2011). Mahalanobis distance-based semi-supervised discriminant analysis for face recognition. Beijing Hangkong Hangtian Daxue xuebao. 37(12). 1589. 1 indexed citations

18.

Shi, Jun. (2009). Research on the content-based image retrieval technology. 9 indexed citations

19.

Shi, Jun. (2005). Reliability and Validity of the Subjective Well-being Scale of Chinese Middle School Students. Zhongguo xinli weisheng zazhi. 1 indexed citations

20.

Shi, Jun. (2005). Reliability and Validation of the General Self-Efficacy Scale. Zhongguo xinli weisheng zazhi. 3 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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