Michael Lippert
Impact in
- Biophysics top 10%
- Cell Image Analysis Techniques
Papers in
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- Radiomics and Machine Learning in Medical Imaging 4
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- Breast Cancer Treatment Studies 3
- Cancer Genomics and Diagnostics 1
- Co-authors
- Johan Hartman (3 shared papers)Mattias Rantalainen (2 shared papers)Jonas Bergh (3 shared papers)Gustav Stålhammar (3 shared papers)Stephanie Robertson (2 shared papers)Lóránd Kis (2 shared papers)Nicholas P. Tobin (1 shared paper)Gustaf Rosin (2 shared papers)
- Journals
- Histopathology (1 paper)Applied immunohistochemistry & molecular morphology (1 paper)Modern Pathology (1 paper)Cytometry Part A (1 paper)Breast Cancer Research and Treatment (1 paper)
- Partner nations
- DenmarkSwedenUnited Kingdom
In The Last Decade
Michael Lippert
9 papers receiving 316 citations
Peers
Comparison fields: 5 of 51
- Health Informatics 10
- Biophysics 34
- Cancer Research 78
- Radiology, Nuclear Medicine and Imaging 86
- Oncology 81
Countries citing papers authored by Michael Lippert
This map shows the geographic impact of Michael Lippert'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 Michael Lippert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Lippert more than expected).
Fields of papers citing papers by Michael Lippert
This network shows the impact of papers produced by Michael Lippert. 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 Michael Lippert. The network helps show where Michael Lippert may publish in the future.
Co-authors
The 25 scholars most cited alongside Michael Lippert, 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 | 2016 | 138 | |
| 2 | 2017 | 74 | |
| 3 | 2020 | 34 | |
| 4 | 2017 | 24 | |
| 5 | 2011 | 20 | |
| 6 | 2018 | 16 | |
| 7 | 2014 | 7 | |
| 8 | 2017 | 4 | |
| 9 | 2016 | 1 |
About Michael Lippert
Michael Lippert is a scholar working on Radiology, Nuclear Medicine and Imaging, Cancer Research, Molecular Biology, Artificial Intelligence and Oncology, having authored 9 papers that have together received 318 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (4 papers), Breast Cancer Treatment Studies (3 papers), AI in cancer detection (3 papers), Gene expression and cancer classification (2 papers), Cell Image Analysis Techniques (2 papers), Colorectal Cancer Screening and Detection (1 paper), Urologic and reproductive health conditions (1 paper) and Cancer Genomics and Diagnostics (1 paper). The work is most often cited by research in Health Informatics (10 citations), Biophysics (34 citations), Cancer Research (78 citations), Radiology, Nuclear Medicine and Imaging (86 citations) and Oncology (81 citations). Michael Lippert has collaborated with scholars based in Denmark, Sweden and United Kingdom. Frequent co-authors include Johan Hartman, Mattias Rantalainen, Jonas Bergh, Gustav Stålhammar, Stephanie Robertson, Lóránd Kis, Nicholas P. Tobin, Gustaf Rosin, Michael Grunkin and Lars Pedersen. Their work appears in journals such as Histopathology, Applied immunohistochemistry & molecular morphology, Modern Pathology, Cytometry Part A and Breast Cancer Research and Treatment.
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.