Liya Ding

5.3k total citations · 1 hit paper
76 papers, 2.3k citations indexed

About

Liya Ding is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Liya Ding has authored 76 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 18 papers in Computational Theory and Mathematics and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Liya Ding's work include Rough Sets and Fuzzy Logic (14 papers), Fuzzy Logic and Control Systems (13 papers) and AI-based Problem Solving and Planning (9 papers). Liya Ding is often cited by papers focused on Rough Sets and Fuzzy Logic (14 papers), Fuzzy Logic and Control Systems (13 papers) and AI-based Problem Solving and Planning (9 papers). Liya Ding collaborates with scholars based in China, United States and Singapore. Liya Ding's co-authors include Aleix M. Martı́nez, Lee Kamentsky, Tim Becker, Derek Thirstrup, Susanne M. Rafelski, Anne E. Carpenter, Juan Carlos Caicedo, Kyle W. Karhohs, Claire McQuin and Vasiliy S. Chernyshev and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Bioinformatics.

In The Last Decade

Liya Ding

68 papers receiving 2.2k citations

Hit Papers

CellProfiler 3.0: Next-generation image processing for bi... 2018 2026 2020 2023 2018 250 500 750 1000
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.

  1. CellProfiler 3.0: Next-generation image processing for biology
    2018 1.2k citations Liya Ding et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Liya Ding China 16 942 487 452 235 213 76 2.3k
Nicolas Chenouard France 17 1.1k 1.2× 491 1.0× 364 0.8× 118 0.5× 242 1.1× 35 2.3k
Michalis Michaelos United States 8 887 0.9× 645 1.3× 211 0.5× 149 0.6× 136 0.6× 8 1.9k
Andrew R. Cohen United States 19 1.1k 1.2× 544 1.1× 400 0.9× 94 0.4× 129 0.6× 44 2.1k
Tim Wang China 2 877 0.9× 638 1.3× 205 0.5× 142 0.6× 135 0.6× 5 1.7k
Lee Kamentsky United States 13 1.5k 1.6× 984 2.0× 329 0.7× 166 0.7× 125 0.6× 21 2.9k
Nicholas Hamilton Australia 28 1.7k 1.8× 298 0.6× 1.1k 2.4× 294 1.3× 143 0.7× 89 3.1k
Carsten Marr Germany 27 1.6k 1.7× 543 1.1× 202 0.4× 386 1.6× 341 1.6× 103 3.1k
Mark‐Anthony Bray United States 24 1.2k 1.2× 656 1.3× 418 0.9× 96 0.4× 127 0.6× 37 2.8k
Tim Becker Germany 15 913 1.0× 865 1.8× 207 0.5× 372 1.6× 365 1.7× 33 2.4k
Kyle W. Karhohs United States 10 1.5k 1.6× 850 1.7× 304 0.7× 344 1.5× 332 1.6× 14 2.7k

Countries citing papers authored by Liya Ding

Since Specialization
Citations

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

Fields of papers citing papers by Liya Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liya Ding

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

All Works

20 of 20 papers shown
1.
Wang, Yuqi, Wenchong Tian, Haolin Yang, et al.. (2025). Knowledge embedding and interpretable machine learning optimize comprehensive benefits for water treatment. npj Clean Water. 8(1).
2.
3.
Ding, Liya, Yawen Wang, Ke Sheng, et al.. (2025). Evaluating ChatGPT’s diagnostic potential for pathology images. Frontiers in Medicine. 11. 1507203–1507203. 2 indexed citations
4.
Li, Youqi, et al.. (2024). Enhancing brain image quality with 3D U-net for stripe removal in light sheet fluorescence microscopy. Brain Informatics. 11(1). 24–24. 2 indexed citations
5.
Jiang, Shengdian, et al.. (2023). NRRS: a re-tracing strategy to refine neuron reconstruction. Bioinformatics Advances. 3(1). vbad054–vbad054. 1 indexed citations
6.
An, Huimin, Liya Ding, Aihua Huang, et al.. (2023). Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions. Diagnostics. 13(10). 1720–1720. 6 indexed citations
7.
Goto, Yuji, et al.. (2023). Gap-filling greenhouse gas fluxes for the closed chamber method at paddy fields using machine learning techniques. Journal of Agricultural Meteorology. 79(4). 142–149. 1 indexed citations
8.
Liu, Yufeng, et al.. (2022). Tracing weak neuron fibers. Bioinformatics. 39(1). 5 indexed citations
9.
Jiang, Shengdian, Yimin Wang, Lijuan Liu, et al.. (2022). Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains. Neuroinformatics. 20(2). 525–536. 13 indexed citations
10.
11.
Guo, Shuxia, et al.. (2021). Image enhancement to leverage the 3D morphological reconstruction of single-cell neurons. Bioinformatics. 38(2). 503–512. 7 indexed citations
12.
Ding, Liya, et al.. (2021). Frost forecast - a practice of machine learning from data. International Journal of Reasoning-based Intelligent Systems. 13(1). 1–1. 1 indexed citations
13.
Costigliola, Nancy, Liya Ding, Christoph J. Burckhardt, et al.. (2017). Vimentin fibers orient traction stress. Proceedings of the National Academy of Sciences. 114(20). 5195–5200. 100 indexed citations
14.
Ding, Liya, et al.. (2010). Application of hybrid logic in inference of Knowware System. 3. 1078–1083. 1 indexed citations
15.
Xu, Ronald X., Kun Huang, Jiwei Huang, et al.. (2010). Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function. Journal of Visualized Experiments. 15 indexed citations
16.
Ding, Liya & Aleix M. Martı́nez. (2010). Features versus Context: An Approach for Precise and Detailed Detection and Delineation of Faces and Facial Features. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32(11). 2022–2038. 90 indexed citations
17.
Ding, Liya & Aleix M. Martı́nez. (2008). Precise detailed detection of faces and facial features. 28. 1–7. 36 indexed citations
18.
Ding, Liya, et al.. (2008). Truth Value Flow Inference in Hybrid KBS Constructed by KWS. 11. 311–311. 4 indexed citations
19.
Ding, Liya, et al.. (1999). Knowledge-Based Approach in Intelligent Handwritten Form Processing. Journal of Advanced Computational Intelligence and Intelligent Informatics. 3(3). 193–199. 1 indexed citations
20.
Ding, Liya, et al.. (1994). Approximate case-based reasoning on neural networks. International Journal of Approximate Reasoning. 10(1). 75–98. 8 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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