Raymond H. Mak
- Cancer Research top 0.05%
- Health Informatics top 0.05%
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- Radiomics and Machine Learning in Medical Imaging 65
- Medical Imaging Techniques and Applications 25
- Pulmonary and Respiratory Medicine top 0.5%
- Lung Cancer Diagnosis and Treatment 70
- Lung Cancer Treatments and Mutations 33
- Molecular Biology top 0.5%
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- Advanced Radiotherapy Techniques 52
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- Lung Cancer Research Studies 15
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- Advanced X-ray and CT Imaging 14
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- Chemotherapy-induced cardiotoxicity and mitigation 13
Raymond H. Mak
186 papers receiving 16.2k citations
Hit Papers
Peers
Comparison fields: 5 of 189
- Cancer Research 6.8k
- Health Informatics 603
- Radiology, Nuclear Medicine and Imaging 5.4k
- Pulmonary and Respiratory Medicine 3.9k
- Molecular Biology 7.6k
Countries citing papers authored by Raymond H. Mak
This map shows the geographic impact of Raymond H. Mak'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 Raymond H. Mak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raymond H. Mak more than expected).
Fields of papers citing papers by Raymond H. Mak
This network shows the impact of papers produced by Raymond H. Mak. 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 Raymond H. Mak. The network helps show where Raymond H. Mak may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Raymond H. Mak, 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 | 2025 | 0 | |
| 2 | 2024 | 7 | |
| 3 | Foundation model for cancer imaging biomarkersbreakdown → | 2024 | 63 |
| 4 | 2024 | 3 | |
| 5 | 2024 | 8 | |
| 6 | 2024 | 0 | |
| 7 | 2023 | 0 | |
| 8 | 2023 | 1 | |
| 9 | 2023 | 6 | |
| 10 | 2023 | 5 | |
| 11 | 2022 | 10 | |
| 12 | 2021 | 4 | |
| 13 | 2021 | 1 | |
| 14 | 2020 | 0 | |
| 15 | Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imagingbreakdown → | 2019 | 402 |
| 16 | 2019 | 0 | |
| 17 | 2017 | 77 | |
| 18 | Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancerbreakdown → | 2017 | 304 |
| 19 | 2017 | 1 | |
| 20 | 2016 | 1 |
About Raymond H. Mak
Raymond H. Mak is a scholar working on Health Informatics, Radiation and Radiology, Nuclear Medicine and Imaging, having authored 203 papers that have together received 16.5k indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (70 papers), Radiomics and Machine Learning in Medical Imaging (65 papers), Advanced Radiotherapy Techniques (52 papers), Lung Cancer Treatments and Mutations (33 papers), Medical Imaging Techniques and Applications (25 papers), Lung Cancer Research Studies (15 papers), Advanced X-ray and CT Imaging (14 papers) and Chemotherapy-induced cardiotoxicity and mitigation (13 papers). The work is most often cited by research in Cancer Research (6.8k citations), Health Informatics (603 citations) and Radiology, Nuclear Medicine and Imaging (5.4k citations). Raymond H. Mak has collaborated with scholars based in United States, Netherlands and Canada. Frequent co-authors include Todd R. Golub, Benjamin L. Ebert, Hugo J.W.L. Aerts, Jun Lü, James R. Downing, Eric A. Miska, Gad Getz, Tyler Jacks, Adolfo A. Ferrando and H. Robert Horvitz. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.
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.