Yu Ji

1.6k total citations
41 papers, 1.0k citations indexed

About

Yu Ji is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Electrical and Electronic Engineering. According to data from OpenAlex, Yu Ji has authored 41 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 15 papers in Radiology, Nuclear Medicine and Imaging and 13 papers in Electrical and Electronic Engineering. Recurrent topics in Yu Ji's work include Radiomics and Machine Learning in Medical Imaging (14 papers), Advanced Memory and Neural Computing (12 papers) and AI in cancer detection (12 papers). Yu Ji is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (14 papers), Advanced Memory and Neural Computing (12 papers) and AI in cancer detection (12 papers). Yu Ji collaborates with scholars based in China and United States. Yu Ji's co-authors include Peifang Liu, Yuan Xie, Youhui Zhang, Hui Li, Maryellen L. Giger, Wenjuan Ma, Wenguang Chen, Heather M. Whitney, Peng Qu and Shuangchen Li and has published in prestigious journals such as Nature, Proceedings of the IEEE and Radiology.

In The Last Decade

Yu Ji

39 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yu Ji China	19	464	411	387	144	89	41	1.0k
Mingu Kang United States	19	291 0.6×	55 0.1×	900 2.3×	158 1.1×	65 0.7×	70	1.2k
William C. Barker United States	12	141 0.3×	247 0.6×	35 0.1×	87 0.6×	56 0.6×	24	694
Maher Rizkalla United States	11	127 0.3×	181 0.4×	98 0.3×	71 0.5×	19 0.2×	138	741
Jiong He Singapore	9	217 0.5×	183 0.4×	57 0.1×	198 1.4×	10 0.1×	19	757
Hidenori Sakanashi Japan	12	309 0.7×	167 0.4×	80 0.2×	97 0.7×	8 0.1×	66	624
Jiayin Zhou China	20	256 0.6×	301 0.7×	115 0.3×	448 3.1×	16 0.2×	91	1.3k
Tetsushi Koide Japan	14	173 0.4×	76 0.2×	303 0.8×	207 1.4×	8 0.1×	153	785
Yulong Yan United States	19	81 0.2×	547 1.3×	137 0.4×	79 0.5×	30 0.3×	88	1.1k
Patrick Maier Germany	17	61 0.1×	37 0.1×	241 0.6×	124 0.9×	117 1.3×	66	877
Paul Salama United States	16	273 0.6×	183 0.4×	53 0.1×	468 3.3×	11 0.1×	115	1.1k

Countries citing papers authored by Yu Ji

Since Specialization

Citations

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

Fields of papers citing papers by Yu Ji

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu Ji

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Ji. A scholar is included among the top collaborators of Yu Ji 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 Yu Ji. Yu Ji 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

Jia, Lee, Tao Fan, Lei Wang, et al.. (2025). SLAMF receptors: key regulators of tumor progression and emerging targets for cancer immunotherapy. Molecular Cancer. 24(1). 145–145. 1 indexed citations

Xu, Tao, Rujing Wang, Mengchao Li, et al.. (2024). A Coordinated Two-Stage Decentralized Flexibility Trading in Distribution Grids with MGs. Protection and Control of Modern Power Systems. 9(5). 54–69. 5 indexed citations

Ma, Xu, et al.. (2024). Research on Grid-Forming Energy Storage System Based on Hybrid Ultracapacitor. 1677–1685.

Ji, Yu, Heather M. Whitney, Hui Li, et al.. (2023). Differences in Molecular Subtype Reference Standards Impact AI-based Breast Cancer Classification with Dynamic Contrast-enhanced MRI. Radiology. 307(1). e220984–e220984. 15 indexed citations

Chen, Yongzi, Rui Zhao, Yu Ji, et al.. (2021). Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer. European Radiology. 32(3). 1676–1687. 21 indexed citations

Ji, Yu, Boxin Li, Rui Zhao, et al.. (2021). The relationship between breast density, age, and mammographic lesion type among Chinese breast cancer patients from a large clinical dataset. BMC Medical Imaging. 21(1). 43–43. 16 indexed citations

Zhang, Youhui, Peng Qu, Yu Ji, et al.. (2020). A system hierarchy for brain-inspired computing. Nature. 586(7829). 378–384. 144 indexed citations

Xie, Xinfeng, Xing Hu, Peng Gu, et al.. (2020). NNBench-X. ACM Transactions on Architecture and Code Optimization. 17(4). 1–25. 2 indexed citations

Ji, Yu, Zixin Liu, & Youhui Zhang. (2020). A Reduced Architecture for ReRAM-Based Neural Network Accelerator and Its Software Stack. IEEE Transactions on Computers. 70(3). 316–331. 4 indexed citations

10.

Ji, Yu, Hui Li, Alexandra Edwards, et al.. (2019). Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution. Cancer Imaging. 19(1). 64–64. 41 indexed citations

11.

Whitney, Heather M., Hui Li, Yu Ji, Peifang Liu, & Maryellen L. Giger. (2019). Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Methods. Proceedings of the IEEE. 108(1). 163–177. 54 indexed citations

12.

Liang, Ling, Lei Deng, Xing Hu, et al.. (2018). Crossbar-Aware Neural Network Pruning. IEEE Access. 6. 58324–58337. 45 indexed citations

13.

Ma, Wenjuan, et al.. (2018). Triple-negative and non-triple-negative breast cancer prediction by mammographic radiomics features. Zhonghua fangshexian yixue zazhi. 52(11). 842–846. 3 indexed citations

14.

Ji, Yu, et al.. (2018). TETRIS: TilE-matching the TRemendous Irregular Sparsity. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 31. 4115–4125. 19 indexed citations

15.

Ma, Wenjuan, Yu Ji, Xinpeng Guo, et al.. (2018). Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features. Academic Radiology. 26(2). 196–201. 106 indexed citations

16.

Ma, Wenjuan, et al.. (2018). Breast cancer Ki67 expression prediction by DCE-MRI radiomics features. Clinical Radiology. 73(10). 909.e1–909.e5. 79 indexed citations

17.

Ji, Yu, et al.. (2018). Correlation between DCE‑MRI radiomics features and Ki‑67 expression in invasive breast cancer. Oncology Letters. 16(4). 5084–5090. 39 indexed citations

18.

Ji, Yu, Youhui Zhang, Shuangchen Li, et al.. (2016). NEUTRAMS: neural network transformation and co-design under neuromorphic hardware constraints. International Symposium on Microarchitecture. 1–13. 35 indexed citations

19.

Liu, Jing, et al.. (2014). Analysis of Mammographic Breast Density in a Group of Screening Chinese Women and Breast Cancer Patients. Asian Pacific Journal of Cancer Prevention. 15(15). 6411–6414. 20 indexed citations

20.

Xu, Jia, et al.. (2011). Optimizations of Multisite Radar System with MIMO Radars for Target Detection. IEEE Transactions on Aerospace and Electronic Systems. 47(4). 2329–2343. 32 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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