Himashi Peiris
Impact in
- Neurology top 10%
- Brain Tumor Detection and Classification
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- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
Papers in
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- Advanced Neural Network Applications 6
- Medical Image Segmentation Techniques 4
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- Radiomics and Machine Learning in Medical Imaging 2
- COVID-19 diagnosis using AI 2
- MRI in cancer diagnosis 1
- Co-authors
- Mehrtash Harandi (7 shared papers)Zhaolin Chen (7 shared papers)Gary F. Egan (7 shared papers)Munawar Hayat (3 shared papers)Mehrdad Arashpour (1 shared paper)Yicheng Wu (1 shared paper)Anthony Tran (1 shared paper)Numan Kutaiba (1 shared paper)
In The Last Decade
Himashi Peiris
8 papers receiving 269 citations
Peers
Comparison fields: 5 of 49
- Neurology 123
- Computer Vision and Pattern Recognition 172
- Radiology, Nuclear Medicine and Imaging 66
- Artificial Intelligence 74
- Nuclear Energy and Engineering 1
Countries citing papers authored by Himashi Peiris
This map shows the geographic impact of Himashi Peiris'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 Himashi Peiris with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Himashi Peiris more than expected).
Fields of papers citing papers by Himashi Peiris
This network shows the impact of papers produced by Himashi Peiris. 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 Himashi Peiris. The network helps show where Himashi Peiris may publish in the future.
Co-authors
The 21 scholars most cited alongside Himashi Peiris, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 148 | |
| 2 | 2023 | 68 | |
| 3 | 2022 | 29 | |
| 4 | 2024 | 18 | |
| 5 | 2023 | 9 | |
| 6 | 2025 | 1 | |
| 7 | 2025 | 1 | |
| 8 | 2025 | 1 | |
| 9 | 2018 | 1 | |
| 10 | 2025 | 0 | |
| 11 | 2025 | 0 |
About Himashi Peiris
Himashi Peiris is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Neurology, Artificial Intelligence and Civil and Structural Engineering, having authored 11 papers that have together received 276 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (6 papers), Medical Image Segmentation Techniques (4 papers), Brain Tumor Detection and Classification (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), COVID-19 diagnosis using AI (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Infrastructure Maintenance and Monitoring (1 paper) and MRI in cancer diagnosis (1 paper). The work is most often cited by research in Neurology (123 citations), Computer Vision and Pattern Recognition (172 citations), Radiology, Nuclear Medicine and Imaging (66 citations), Artificial Intelligence (74 citations) and Nuclear Energy and Engineering (1 citation). Himashi Peiris has collaborated with scholars based in Australia and China. Frequent co-authors include Mehrtash Harandi, Zhaolin Chen, Gary F. Egan, Munawar Hayat, Mehrdad Arashpour, Yicheng Wu, Anthony Tran, Numan Kutaiba, Zhaolin Chen and Meng Law. Their work appears in journals such as NMR in Biomedicine, Resources Conservation and Recycling, Neurocomputing, Nature Machine Intelligence and Lecture notes in computer science.
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.