Aimon Rahman
Impact in
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- Digital Imaging for Blood Diseases
- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- Digital Imaging for Blood Diseases 5
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- AI in cancer detection 4
- Domain Adaptation and Few-Shot Learning 1
- Imbalanced Data Classification Techniques 1
- Co-authors
- M. R. C. Mahdy (5 shared papers)Vishal M. Patel (1 shared paper)Jeya Maria Jose Valanarasu (1 shared paper)Ilker Hacihaliloglu (1 shared paper)M. Sohel Rahman (3 shared papers)Nabeel Mohammed (1 shared paper)
- Journals
- Computers in Biology and Medicine (2 papers)Tissue and Cell (2 papers)Biomedical Physics & Engineering Express (1 paper)CLEF (Working Notes) (1 paper)
- Partner nations
- BangladeshCanadaUnited States
In The Last Decade
Aimon Rahman
8 papers receiving 222 citations
Aimon Rahman's Hit Papers
Peers
Comparison fields: 5 of 59
- Computer Vision and Pattern Recognition 118
- Radiology, Nuclear Medicine and Imaging 93
- Biophysics 19
- Artificial Intelligence 97
- Neurology 23
Countries citing papers authored by Aimon Rahman
This map shows the geographic impact of Aimon Rahman'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 Aimon Rahman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aimon Rahman more than expected).
Fields of papers citing papers by Aimon Rahman
This network shows the impact of papers produced by Aimon Rahman. 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 Aimon Rahman. The network helps show where Aimon Rahman may publish in the future.
Co-authors
The 6 scholars most cited alongside Aimon Rahman, 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 | Ambiguous Medical Image Segmentation Using Diffusion Models Hit paper breakdown → | 2023 | 90 |
| 2 | 2021 | 38 | |
| 3 | 2020 | 37 | |
| 4 | 2022 | 26 | |
| 5 | 2021 | 21 | |
| 6 | Estimating Severity from CT Scans of Tuberculosis Patients using 3D Convolutional Nets and Slice Selection. | 2019 | 6 |
| 7 | 2021 | 6 | |
| 8 | 2019 | 1 |
About Aimon Rahman
Aimon Rahman is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Ecology and Public Health, Environmental and Occupational Health, having authored 8 papers that have together received 225 indexed citations. Recurring topics across this work include Digital Imaging for Blood Diseases (5 papers), AI in cancer detection (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Domain Adaptation and Few-Shot Learning (1 paper), COVID-19 diagnosis using AI (1 paper), Imbalanced Data Classification Techniques (1 paper), Cell Image Analysis Techniques (1 paper) and Image Processing Techniques and Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (118 citations), Radiology, Nuclear Medicine and Imaging (93 citations), Biophysics (19 citations), Artificial Intelligence (97 citations) and Neurology (23 citations). Aimon Rahman has collaborated with scholars based in Bangladesh, Canada and United States. Frequent co-authors include M. R. C. Mahdy, Vishal M. Patel, Jeya Maria Jose Valanarasu, Ilker Hacihaliloglu, M. Sohel Rahman and Nabeel Mohammed. Their work appears in journals such as Computers in Biology and Medicine, Tissue and Cell, Biomedical Physics & Engineering Express and CLEF (Working Notes).
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.