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Researcher · Monash University · Australia

Himashi Peiris

Himashi Peiris publishes mostly in Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Neurology, on topics such as Advanced Neural Network Applications, Brain Tumor Detection and Classification and Medical Image Segmentation Techniques.

ORCID 0000-0003-0464-1182 OpenAlex
World rank
#2,007,584
of 7,710,434 ranked researchers
Rank in Australia
#59,003
of 140,210
Works
28
Citations
481
Citations per work
17.2
h-index
8
i10-index 8Top 60% in Computer Vision and Pattern Recognition
Himashi Peiris

What does Himashi Peiris research?

Himashi Peiris's work is mostly in Computer Science (44%), Neuroscience (17%), Medicine (17%).

Physical Sciences: 13 works · 47.0% of their workComputer Science: 12 works · 44.5% of their workComputer Vision and Pattern Recognition: 11 works · 38.3% of their workArtificial Intelligence: 2 works · 6.2% of their workEngineering: 1 works · 2.5% of their workBiomedical Engineering: 1 works · 2.5% of their workEverything else: 5 works · 18.4% of their workLife Sciences: 5 works · 17.3% of their workNeuroscience: 5 works · 17.3% of their workNeurology: 4 works · 13.6% of their workCellular and Molecular Neuroscience: 1 works · 3.7% of their workHealth Sciences: 5 works · 17.3% of their workMedicine: 5 works · 17.3% of their workRadiology, Nuclear Medicine and Imaging: 5 works · 17.3% of their workTHEIR WORKHimashi Peiris28works
Life Sciences17%Physical Sciences47%Health Sciences17%

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 Science1244.5%
    2. Engineering12.5%
  2. Everything else518.4%
    1. Neuroscience517.3%
    1. Medicine517.3%
  1. 1Computer Vision and Pattern Recognition38% of their works
  2. 2Radiology, Nuclear Medicine and Imaging17% of their works
  3. 3Neurology14% of their works
  4. 4Artificial Intelligence6% of their works
  5. 5Cellular and Molecular Neuroscience4% of their works
  6. 6Biomedical Engineering2% of their works

Research topics

Which keywords describe Himashi Peiris's research?

The keywords of their largest research topics: Deep Learning, Image Segmentation, Machine Learning, Convolutional Neural Networks, Feature Extraction, Medical Imaging, Image Recognition and Object Detection.

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 24 words, with their numbers
  1. Deep Learning415 topics
  2. Image Segmentation212 topics
  3. Machine Learning183 topics
  4. Convolutional Neural Networks172 topics
  5. Feature Extraction152 topics
  6. Medical Imaging152 topics
  7. Image Recognition141 topic
  8. Object Detection141 topic
  9. Texture Analysis142 topics
  10. Medical Image Analysis132 topics
  11. Brain Tumor111 topic
  12. MRI111 topic
  13. Active Contours101 topic
  14. Graph Cuts101 topic
  15. Quantitative Analysis41 topic
  16. Radiomics41 topic
  17. Cerebrospinal Fluid31 topic
  18. Computer-Aided Detection31 topic
  19. Generative Adversarial Networks31 topic
  20. Glymphatic System31 topic
  21. Histopathology Images31 topic
  22. Image Synthesis31 topic
  23. Imaging31 topic
  24. PET/CT31 topic

Where does Himashi Peiris work?

Himashi Peiris's main affiliation in the publication record is Monash University, Australia.

How many publications and citations does Himashi Peiris have?

OpenAlex counts 28 works and 481 citations for Himashi Peiris, an h-index of 8, ranking #2,007,584 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.

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