Tong Jin

427 total citations
13 papers, 268 citations indexed

About

Tong Jin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Tong Jin has authored 13 papers receiving a total of 268 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Signal Processing. Recurrent topics in Tong Jin's work include Privacy-Preserving Technologies in Data (3 papers), AI in cancer detection (3 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Tong Jin is often cited by papers focused on Privacy-Preserving Technologies in Data (3 papers), AI in cancer detection (3 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Tong Jin collaborates with scholars based in China, United Kingdom and United States. Tong Jin's co-authors include Fang Liu, Robert B. Bohn, Siguang Chen, Chun‐Li Cao, Qiaoli Li, Jingyun Zhang, Shenghui Liu, Jun Li, Chunlai Du and Kai Wang and has published in prestigious journals such as Journal of Ethnopharmacology, Journal of Affective Disorders and IEEE Transactions on Vehicular Technology.

In The Last Decade

Tong Jin

12 papers receiving 251 citations

Peers

Tong Jin

IS

CNC

AI

RNMI

EDM

N. M. Saravana Kumar India

Amrutanshu Panigrahi India

Anand Mahendran India

Manish Arya India

Jinhua Ma China

S. V. Subrahmanya India

Wu Zhang China

Rashid Al‐Ali Qatar

Poonam Rani India

Babak Bashari Rad Malaysia

N. M. Saravana Kumar India

Tong Jin

67 ×0.5

IS

66 ×0.7

CNC

136 ×1.9

AI

23 ×0.8

RNMI

8 ×0.3

EDM

Citations per year, relative to Tong Jin Tong Jin (= 1×) peers N. M. Saravana Kumar

Countries citing papers authored by Tong Jin

Since Specialization

Citations

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

Fields of papers citing papers by Tong Jin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tong Jin

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

13 of 13 papers shown

1.

Hu, S. L., et al.. (2025). Development and application of a machine learning-based antenatal depression prediction model. Journal of Affective Disorders. 375. 137–147. 1 indexed citations

2.

Jin, Tong, Jun Li, Chun‐Li Cao, et al.. (2024). Using the GoogLeNet deep-learning model to distinguish between benign and malignant breast masses based on conventional ultrasound: a systematic review and meta-analysis. Quantitative Imaging in Medicine and Surgery. 14(10). 7111–7127. 2 indexed citations

3.

Cao, Chun‐Li, Qiaoli Li, Tong Jin, et al.. (2023). Artificial intelligence in thyroid ultrasound. Frontiers in Oncology. 13. 1060702–1060702. 25 indexed citations

4.

Jin, Tong & Siguang Chen. (2023). Decentralized Two-Stage Federated Learning with Knowledge Transfer. 3181–3186. 2 indexed citations

5.

Jin, Tong, et al.. (2023). Metadata and Image Features Co-Aware Personalized Federated Learning for Smart Healthcare. IEEE Journal of Biomedical and Health Informatics. 27(8). 4110–4119. 17 indexed citations

6.

Chen, Siguang, et al.. (2023). Metadata and Image Features Co-Aware Semi-Supervised Vertical Federated Learning With Attention Mechanism. IEEE Transactions on Vehicular Technology. 73(2). 2520–2532. 6 indexed citations

7.

Du, Chunlai, Tong Jin, Yanhui Guo, Binghao Jia, & Bin Li. (2021). FastAFLGo: Toward a Directed Greybox Fuzzing. Computers, materials & continua/Computers, materials & continua (Print). 69(3). 3845–3855. 1 indexed citations

8.

Li, Qiaoli, Wen Liu, Ming Chen, et al.. (2021). The value of contrast‐enhanced ultrasound for the diagnosis of metastatic cervical lymph nodes of papillary thyroid carcinoma: A systematic review and meta‐analysis. Journal of Clinical Ultrasound. 50(1). 60–69. 10 indexed citations

9.

Jin, Tong, et al.. (2020). Febuxostat Use and Risks of Cardiovascular Disease Events, Cardiac Death, and All-cause Mortality: Metaanalysis of Randomized Controlled Trials. The Journal of Rheumatology. 48(7). 1082–1089. 4 indexed citations

10.

Zhang, Jingyun, Kai Wang, Tong Jin, et al.. (2020). The effect of total lignans from Fructus Arctii on Streptozotocin-induced diabetic retinopathy in Wistar rats. Journal of Ethnopharmacology. 255. 112773–112773. 24 indexed citations

11.

Du, Chunlai, et al.. (2020). Using Object Detection Network for Malware Detection and Identification in Network Traffic Packets. Computers, materials & continua/Computers, materials & continua (Print). 64(3). 1785–1796. 14 indexed citations

12.

Bohn, Robert B., et al.. (2011). NIST Cloud Computing Reference Architecture. 594–596. 149 indexed citations

13.

Hogan, Michael D., et al.. (2011). NIST-SP 500-291, NIST Cloud Computing Standards Roadmap | NIST. 13 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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