John Heine
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
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- Digital Radiography and Breast Imaging
Papers in
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- AI in cancer detection 35
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- Digital Radiography and Breast Imaging 36
- Co-authors
- Robert P. VelthuizenLaurence P. ClarkeMohan VaidyanathanMarc A. CamachoRobert W. ThatcherLawrence HallM.L. SilbigerPoonam Malhotra
- Journals
- Medical Physics (11 papers)Academic Radiology (7 papers)Cancer Epidemiology Biomarkers & Prevention (6 papers)BioMedical Engineering OnLine (4 papers)Scientific Reports (3 papers)
- Partner nations
- United StatesChinaJapan
In The Last Decade
John Heine
70 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 124
- Radiology, Nuclear Medicine and Imaging 750
- Pulmonary and Respiratory Medicine 994
- Artificial Intelligence 899
- Computer Vision and Pattern Recognition 474
- Oncology 518
Countries citing papers authored by John Heine
This map shows the geographic impact of John Heine'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 John Heine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Heine more than expected).
Fields of papers citing papers by John Heine
This network shows the impact of papers produced by John Heine. 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 John Heine. The network helps show where John Heine may publish in the future.
Co-authorship network
The 25 scholars most cited alongside John Heine, 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 | 2024 | 1 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 0 | |
| 4 | 2023 | 3 | |
| 5 | 2023 | 4 | |
| 6 | 2020 | 8 | |
| 7 | 2019 | 16 | |
| 8 | 2018 | 0 | |
| 9 | 2017 | 36 | |
| 10 | 2017 | 60 | |
| 11 | 2015 | 18 | |
| 12 | 2015 | 141 | |
| 13 | 2014 | 14 | |
| 14 | 乳房画像報告データシステム(BI-RADS)乳房組成デスクリプタ: フルフィールドデジタルマンモグラフィーのための自動測定開発 | 2013 | 15 |
| 15 | 2012 | 64 | |
| 16 | 2011 | 5 | |
| 17 | 2011 | 23 | |
| 18 | 2011 | 12 | |
| 19 | 2008 | 1 | |
| 20 | 2006 | 25 |
About John Heine
John Heine is a scholar working on Artificial Intelligence, Pulmonary and Respiratory Medicine, Oncology, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 76 papers that have together received 2.0k indexed citations. Recurring topics across this work include Digital Radiography and Breast Imaging (36 papers), AI in cancer detection (35 papers), Global Cancer Incidence and Screening (18 papers), Medical Imaging Techniques and Applications (11 papers), Cancer Risks and Factors (8 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Image and Signal Denoising Methods (7 papers) and Gene expression and cancer classification (6 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (750 citations), Pulmonary and Respiratory Medicine (994 citations), Artificial Intelligence (899 citations), Computer Vision and Pattern Recognition (474 citations) and Oncology (518 citations). John Heine has collaborated with scholars based in United States, China and Japan. Frequent co-authors include Robert P. Velthuizen, Laurence P. Clarke, Mohan Vaidyanathan, Marc A. Camacho, Robert W. Thatcher, Lawrence Hall, M.L. Silbiger, Poonam Malhotra, Celine M. Vachon and Christopher G. Scott. Their work appears in journals such as Medical Physics, Academic Radiology, Cancer Epidemiology Biomarkers & Prevention, BioMedical Engineering OnLine and Scientific Reports.
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.