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Researcher · Vanderbilt University · United States

Ruining Deng

Ruining Deng publishes mostly in Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging, on topics such as AI in cancer detection, Cell Image Analysis Techniques and Medical Image Segmentation Techniques.

ORCID 0000-0001-6300-8518 OpenAlex
World rank
#1,212,063
of 7,710,434 ranked researchers
Rank in United States
#388,439
of 1,627,098
Works
179
Citations
991
Citations per work
5.5
h-index
16
i10-index 19Top 21% in Computer Vision and Pattern Recognition
Ruining Deng

What does Ruining Deng research?

Ruining Deng's work is mostly in Computer Science (50%), Biochemistry, Genetics and Molecular Biology (15%), Medicine (13%).

Physical Sciences: 90 works · 50.3% of their workComputer Science: 90 works · 50.3% of their workComputer Vision and Pattern Recognition: 46 works · 25.5% of their workArtificial Intelligence: 44 works · 24.8% of their workEverything else: 39 works · 21.7% of their workLife Sciences: 27 works · 14.9% of their workBiochemistry, Genetics and Molecular Biology: 27 works · 14.9% of their workBiophysics: 15 works · 8.2% of their workMolecular Biology: 12 works · 6.7% of their workHealth Sciences: 23 works · 13.1% of their workMedicine: 23 works · 13.1% of their workRadiology, Nuclear Medicine and Imaging: 18 works · 9.9% of their workOncology: 6 works · 3.2% of their workTHEIR WORKRuining Deng179works
Life Sciences15%Physical Sciences50%Health Sciences13%

Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.

The fields and subfields, with their numbers
    1. Computer Science9050.3%
  2. Everything else3921.7%
    1. Biochemistry, Genetics and Molecular Biology2714.9%
    1. Medicine2313.1%
  1. 1Computer Vision and Pattern Recognition26% of their works
  2. 2Artificial Intelligence25% of their works
  3. 3Radiology, Nuclear Medicine and Imaging10% of their works
  4. 4Biophysics8% of their works
  5. 5Molecular Biology7% of their works
  6. 6Oncology3% of their works

Research topics

Which keywords describe Ruining Deng's research?

The keywords of their largest research topics: Deep Learning, Machine Learning, Medical Image Analysis, Convolutional Neural Networks, Computer-Aided Detection, Histopathology Images, Image Processing and Texture Analysis.

Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most.

All 23 words, with their numbers
  1. Deep Learning1804 topics
  2. Machine Learning1734 topics
  3. Medical Image Analysis1433 topics
  4. Convolutional Neural Networks1403 topics
  5. Computer-Aided Detection911 topic
  6. Histopathology Images911 topic
  7. Image Processing632 topics
  8. Texture Analysis462 topics
  9. Feature Extraction442 topics
  10. Bioimage Analysis381 topic
  11. High-Content Screening381 topic
  12. Graph Cuts271 topic
  13. Image Segmentation271 topic
  14. Leukemia Classification251 topic
  15. Malaria Parasite Detection251 topic
  16. Image Recognition241 topic
  17. Object Detection241 topic
  18. Medical Imaging191 topic
  19. Radiomics191 topic
  20. Single-Cell191 topic
  21. Transcriptomics191 topic
  22. Colorectal Cancer141 topic
  23. Screening141 topic

Where does Ruining Deng work?

Ruining Deng's main affiliation in the publication record is Vanderbilt University, United States.

How many publications and citations does Ruining Deng have?

OpenAlex counts 179 works and 991 citations for Ruining Deng, an h-index of 16, ranking #1,212,063 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.

Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.

Discussion

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