Yuanpu Xie

3.1k total citations
21 papers, 1.4k citations indexed

About

Yuanpu Xie is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Yuanpu Xie has authored 21 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Yuanpu Xie's work include AI in cancer detection (11 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Cell Image Analysis Techniques (5 papers). Yuanpu Xie is often cited by papers focused on AI in cancer detection (11 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Cell Image Analysis Techniques (5 papers). Yuanpu Xie collaborates with scholars based in United States, Italy and China. Yuanpu Xie's co-authors include Fuyong Xing, Lin Yang, Hai Su, Fujun Liu, Xiangfei Kong, Xiaoshuang Shi, Zizhao Zhang, Manish Sapkota, Pingjun Chen and Lin Yang and has published in prestigious journals such as IEEE Transactions on Medical Imaging, BMC Bioinformatics and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Yuanpu Xie

21 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yuanpu Xie United States	13	901	598	488	309	144	21	1.4k
Abhishek Vahadane India	8	1.0k 1.1×	708 1.2×	590 1.2×	276 0.9×	93 0.6×	10	1.3k
Humayun Irshad United States	13	999 1.1×	728 1.2×	499 1.0×	437 1.4×	112 0.8×	20	1.5k
Laura E. Boucheron United States	12	1.2k 1.4×	844 1.4×	552 1.1×	386 1.2×	120 0.8×	40	1.8k
Shadi Albarqouni Germany	17	1.0k 1.1×	577 1.0×	706 1.4×	166 0.5×	70 0.5×	49	1.6k
Yee‐Wah Tsang United Kingdom	5	857 1.0×	429 0.7×	551 1.1×	224 0.7×	65 0.5×	6	1.2k
Ruchika Verma United States	10	583 0.6×	463 0.8×	499 1.0×	161 0.5×	85 0.6×	30	1.0k
Neeraj Kumar India	8	649 0.7×	548 0.9×	394 0.8×	190 0.6×	131 0.9×	27	967
Shan E Ahmed Raza United Kingdom	19	1.3k 1.4×	727 1.2×	884 1.8×	355 1.1×	144 1.0×	55	2.1k
Ángel Cruz-Roa Colombia	13	1.4k 1.5×	776 1.3×	852 1.7×	206 0.7×	73 0.5×	40	1.8k

Countries citing papers authored by Yuanpu Xie

Since Specialization

Citations

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

Fields of papers citing papers by Yuanpu Xie

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuanpu Xie

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

1.

Anelli, Vito Walter, Bruce Ferwerda, Luca Belli, et al.. (2021). RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter’s Home Timeline. 819–824. 4 indexed citations

2.

Belli, Luca, Alykhan Tejani, Yuanpu Xie, et al.. (2021). The 2021 RecSys Challenge Dataset: Fairness is not optional. 1–6. 7 indexed citations

3.

Belli, Luca, Sofia Ira Ktena, Alykhan Tejani, et al.. (2020). Privacy-Preserving Recommender Systems Challenge on Twitter's Home Timeline. arXiv (Cornell University). 2 indexed citations

4.

Zhang, Caojin, Yuanpu Xie, Sofia Ira Ktena, et al.. (2020). Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems. 521–526. 25 indexed citations

5.

Anelli, Vito Walter, Amra Delić, Nazareno Andrade, et al.. (2020). RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter’s Home Timeline. 623–627. 8 indexed citations

6.

Shi, Xiaoshuang, Fuyong Xing, Yuanpu Xie, et al.. (2020). Loss-Based Attention for Deep Multiple Instance Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 5742–5749. 56 indexed citations

7.

Xing, Fuyong, Yuanpu Xie, Xiaoshuang Shi, et al.. (2019). Towards pixel-to-pixel deep nucleus detection in microscopy images. BMC Bioinformatics. 20(1). 472–472. 30 indexed citations

8.

Zhang, Zizhao, Pingjun Chen, Mason McGough, et al.. (2019). Publisher Correction: Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nature Machine Intelligence. 1(6). 289–289. 2 indexed citations

9.

Zhang, Zizhao, Pingjun Chen, Mason McGough, et al.. (2019). Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nature Machine Intelligence. 1(5). 236–245. 189 indexed citations

10.

Zhang, Zizhao, Pingjun Chen, Mason McGough, et al.. (2019). Publisher Correction: Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nature Machine Intelligence. 1(8). 384–384. 3 indexed citations

11.

Shi, Xiaoshuang, et al.. (2017). Supervised graph hashing for histopathology image retrieval and classification. Medical Image Analysis. 42. 117–128. 33 indexed citations

12.

Xie, Yuanpu, Fuyong Xing, Xiaoshuang Shi, et al.. (2017). Efficient and robust cell detection: A structured regression approach. Medical Image Analysis. 44. 245–254. 89 indexed citations

13.

Sapkota, Manish, Fujun Liu, Yuanpu Xie, et al.. (2017). AIIMDs: An Integrated Framework of Automatic Idiopathic Inflammatory Myopathy Diagnosis for Muscle. IEEE Journal of Biomedical and Health Informatics. 22(3). 942–954. 2 indexed citations

14.

Xing, Fuyong, Yuanpu Xie, Hai Su, Fujun Liu, & Lin Yang. (2017). Deep Learning in Microscopy Image Analysis: A Survey. IEEE Transactions on Neural Networks and Learning Systems. 29(10). 4550–4568. 291 indexed citations

15.

Xing, Fuyong, Xiaoshuang Shi, Zizhao Zhang, et al.. (2016). Transfer Shape Modeling Towards High-Throughput Microscopy Image Segmentation. Lecture notes in computer science. 9902. 183–190. 7 indexed citations

16.

Xie, Yuanpu, Zizhao Zhang, Manish Sapkota, & Lin Yang. (2016). Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation. Lecture notes in computer science. 9901. 185–193. 48 indexed citations

17.

Su, Hai, Fuyong Xing, Xiangfei Kong, et al.. (2015). Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders. Lecture notes in computer science. 9351. 383–390. 61 indexed citations

18.

Xie, Yuanpu, Fuyong Xing, Xiangfei Kong, Hai Su, & Lin Yang. (2015). Beyond Classification: Structured Regression for Robust Cell Detection Using Convolutional Neural Network. Lecture notes in computer science. 9351. 358–365. 125 indexed citations

19.

Xie, Yuanpu, Xiangfei Kong, Fuyong Xing, et al.. (2015). Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images. Lecture notes in computer science. 9351. 374–382. 49 indexed citations

20.

Xing, Fuyong, Yuanpu Xie, & Lin Yang. (2015). An Automatic Learning-Based Framework for Robust Nucleus Segmentation. IEEE Transactions on Medical Imaging. 35(2). 550–566. 262 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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