Lin Ma
Impact in
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- Digital Radiography and Breast Imaging
- Artificial Intelligence top 2%
- AI in cancer detection
- Anomaly Detection Techniques and Applications
Papers in
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- Digital Radiography and Breast Imaging 17
- Oncology 15
- Cancer Cells and Metastasis 5
- Cancer Immunotherapy and Biomarkers 3
- Cancer Risks and Factors 3
- Co-authors
- Karla Kerlikowske (22 shared papers)John Shepherd (17 shared papers)Celine M. Vachon (16 shared papers)Steven R. Cummings (10 shared papers)Xuan Xia (1 shared paper)Nan Li (1 shared paper)Xizhou Pan (1 shared paper)Xing He (1 shared paper)
- Journals
- Cancer Epidemiology Biomarkers & Prevention (6 papers)Breast Cancer Research and Treatment (5 papers)Breast Cancer Research (4 papers)Cancer Research (3 papers)Neurocomputing (2 papers)
- Partner nations
- United StatesChinaGermany
In The Last Decade
Lin Ma
62 papers receiving 1.6k citations
Lin Ma's Hit Papers
Peers
Comparison fields: 5 of 126
- Pulmonary and Respiratory Medicine 593
- Artificial Intelligence 503
- Oncology 370
- Cancer Research 143
- Radiology, Nuclear Medicine and Imaging 172
Countries citing papers authored by Lin Ma
This map shows the geographic impact of Lin Ma'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 Lin Ma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lin Ma more than expected).
Fields of papers citing papers by Lin Ma
This network shows the impact of papers produced by Lin Ma. 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 Lin Ma. The network helps show where Lin Ma may publish in the future.
Co-authors
The 25 scholars most cited alongside Lin Ma, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 67 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | GAN-based anomaly detection: A review Hit paper breakdown → | 2022 | 260 |
| 2 | 2015 | 143 | |
| 3 | 2013 | 140 | |
| 4 | 2011 | 132 | |
| 5 | 2016 | 105 | |
| 6 | 2008 | 96 | |
| 7 | 2018 | 75 | |
| 8 | 2012 | 61 | |
| 9 | 2018 | 55 | |
| 10 | 2011 | 46 | |
| 11 | 2015 | 39 | |
| 12 | 2017 | 38 | |
| 13 | 2017 | 38 | |
| 14 | 2016 | 38 | |
| 15 | 2017 | 36 | |
| 16 | 2016 | 28 | |
| 17 | 2015 | 27 | |
| 18 | 2019 | 25 | |
| 19 | 2019 | 18 | |
| 20 | 2019 | 17 |
About Lin Ma
Lin Ma is a scholar working on Pulmonary and Respiratory Medicine, Oncology, Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging, having authored 67 papers that have together received 1.6k indexed citations. Recurring topics across this work include Digital Radiography and Breast Imaging (17 papers), Breast Cancer Treatment Studies (5 papers), Cancer Cells and Metastasis (5 papers), Effects of Radiation Exposure (4 papers), DNA Repair Mechanisms (3 papers), Advanced Radiotherapy Techniques (3 papers), Cancer Immunotherapy and Biomarkers (3 papers) and Cancer Risks and Factors (3 papers). The work is most often cited by research in Pulmonary and Respiratory Medicine (593 citations), Artificial Intelligence (503 citations), Oncology (370 citations), Cancer Research (143 citations) and Radiology, Nuclear Medicine and Imaging (172 citations). Lin Ma has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Karla Kerlikowske, John Shepherd, Celine M. Vachon, Steven R. Cummings, Xuan Xia, Nan Li, Xizhou Pan, Xing He, Matthew R. Jensen and Ning Ding. Their work appears in journals such as Cancer Epidemiology Biomarkers & Prevention, Breast Cancer Research and Treatment, Breast Cancer Research, Cancer Research and Neurocomputing.
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.