Chaofan Chen

3.1k total citations · 1 hit paper
40 papers, 1.3k citations indexed

About

Chaofan Chen is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Chaofan Chen has authored 40 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 8 papers in Computer Networks and Communications and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Chaofan Chen's work include Explainable Artificial Intelligence (XAI) (8 papers), Multimodal Machine Learning Applications (5 papers) and AI in cancer detection (5 papers). Chaofan Chen is often cited by papers focused on Explainable Artificial Intelligence (XAI) (8 papers), Multimodal Machine Learning Applications (5 papers) and AI in cancer detection (5 papers). Chaofan Chen collaborates with scholars based in China, United States and Hong Kong. Chaofan Chen's co-authors include Cynthia Rudin, Oscar Li, Zhi Chen, Chudi Zhong, Haiyang Huang, Lesia Semenova, Hao Liu, Alina Jade Barnett, Jon Donnelly and Changsheng Xu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Bioinformatics and Scientific Reports.

In The Last Decade

Chaofan Chen

30 papers receiving 1.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Interpretable machine learning: Fundamental principles and 10 grand challenges

2022 440 citations Cynthia Rudin, Chaofan Chen et al. profile →

Peers

Chaofan Chen

AI

CVPR

PS

HI

RNMI

Gaurav Gupta India

Md. Mohsin Kabir Bangladesh

Robert Geirhos Germany

Claudio Michaelis Germany

Anna Saranti Austria

Nagwan Abdel Samee Saudi Arabia

Fredrik Johansson Sweden

Sylvain Gugger United States

Mahendra Kumar Gourisaria India

Gaurav Gupta India

Chaofan Chen

376 ×0.5

AI

252 ×1.1

CVPR

121 ×1.1

PS

42 ×0.4

HI

224 ×2.3

RNMI

Citations per year, relative to Chaofan Chen Chaofan Chen (= 1×) peers Gaurav Gupta

Countries citing papers authored by Chaofan Chen

Since Specialization

Citations

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

Fields of papers citing papers by Chaofan Chen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chaofan Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Chaofan Chen. A scholar is included among the top collaborators of Chaofan Chen 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 Chaofan Chen. Chaofan Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Donnelly, Jon, et al.. (2025). Creating interpretable deep learning models to identify species using environmental DNA sequences. Scientific Reports. 15(1). 27436–27436. 1 indexed citations

2.

Chen, Chaofan, et al.. (2025). Context-guided segmentation for histopathologic cancer segmentation. Scientific Reports. 15(1). 5404–5404.

3.

Donnelly, Jon, et al.. (2025). Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time. 4528–4538.

4.

Chen, Chaofan, et al.. (2025). Challenges and opportunities in using interpretable AI to develop relationship interventions. Family Relations. 74(3). 1299–1322. 1 indexed citations

5.

Chen, Chaofan, Yubo Song, Yu Jiang, & Mingming Zhang. (2024). Optimizing Network Service Continuity with Quality-Driven Resource Migration. Electronics. 13(9). 1666–1666.

6.

Yang, Yamin, Weitao Li, Chaofan Chen, et al.. (2024). Fiber optic-based integrated system for in vivo multiscale pharmacokinetic monitoring. Biomedical Optics Express. 15(6). 3770–3770.

7.

Chen, Chaofan, et al.. (2024). Semi-supervised Online Elastic Stochastic Configuration Network to Deal with Concept Drift and Class Imbalance in Data Streams. 1–8.

8.

Yang, Julia, Alina Jade Barnett, Jon Donnelly, et al.. (2024). FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography. 5003–5009. 1 indexed citations

9.

Barnett, Alina Jade, Fides R. Schwartz, Chaofan Chen, et al.. (2023). A user interface to communicate interpretable AI decisions to radiologists. 23–23. 2 indexed citations

10.

Wang, Zhisheng, Gang Huang, Bing Li, et al.. (2022). Effects of different physical activities on brain-derived neurotrophic factor: A systematic review and bayesian network meta-analysis. Frontiers in Aging Neuroscience. 14. 981002–981002. 27 indexed citations

11.

Chen, Chaofan, Ehsan Abadi, Wanyi Fu, et al.. (2022). A patient-informed approach to predict iodinated-contrast media enhancement in the liver. European Journal of Radiology. 156. 110555–110555.

12.

Li, Ya, et al.. (2022). Long Short-Term Memory-Based Music Analysis System for Music Therapy. Frontiers in Psychology. 13. 928048–928048. 5 indexed citations

13.

Donnelly, Jon, Alina Jade Barnett, & Chaofan Chen. (2022). Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 10255–10265. 53 indexed citations

14.

Chen, Chaofan, et al.. (2022). An Adaptive Data Rate-Based Task Offloading Scheme in Vehicular Networks. 877–884. 1 indexed citations

15.

Chen, Chaofan, et al.. (2021). On a New SNR Estimation Approach With Polar Codes. 727–732. 2 indexed citations

16.

Chen, Chaofan, et al.. (2021). A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations. Decision Support Systems. 152. 113647–113647. 30 indexed citations

17.

Wu, Jiayi, Chaofan Chen, & Xiangyuan Bu. (2020). Polar Code-Aided Frequency Offset Estimation Algorithm in Satellite Covert Communication Carrier Synchronization. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1340–1344. 2 indexed citations

18.

Chen, Chaofan, Steffen Vanneste, & Xu Chen. (2020). Review: Membrane tethers control plasmodesmal function and formation. Plant Science. 304. 110800–110800. 6 indexed citations

19.

Huang, Dingquan, Yanbiao Sun, Zhiming Ma, et al.. (2019). Salicylic acid-mediated plasmodesmal closure via Remorin-dependent lipid organization. Proceedings of the National Academy of Sciences. 116(42). 21274–21284. 111 indexed citations

20.

Chen, Chaofan, Oscar Li, Xiaohui Tao, et al.. (2019). This Looks Like That: Deep Learning for Interpretable Image Recognition. Neural Information Processing Systems. 32. 8928–8939. 76 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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