Anton Böhm
Impact in
- Biophysics top 0.5%
- Cell Image Analysis Techniques
- Advanced Fluorescence Microscopy Techniques
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- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
Papers in
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- Cell Image Analysis Techniques 3
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- Advanced Neural Network Applications 3
- Medical Image Segmentation Techniques 2
- Co-authors
- Olaf Ronneberger (3 shared papers)Philipp Kainz (1 shared paper)Martin Urschler (1 shared paper)Xiaojuan Qi (1 shared paper)Nasir Rajpoot (1 shared paper)Josien P. W. Pluim (1 shared paper)Bassem Ben Cheikh (1 shared paper)David R. J. Snead (1 shared paper)
- Journals
- Nature Methods (1 paper)eLife (1 paper)Medical Image Analysis (1 paper)IEEE International Symposium on Biomedical Imaging (ISBI) (3 papers)
- Partner nations
- GermanySwitzerlandUnited Kingdom
In The Last Decade
Anton Böhm
6 papers receiving 2.2k citations
Anton Böhm's Hit Papers
Peers
Comparison fields: 5 of 121
- Biophysics 509
- Computer Vision and Pattern Recognition 727
- Health Informatics 57
- Artificial Intelligence 624
- Structural Biology 27
Countries citing papers authored by Anton Böhm
This map shows the geographic impact of Anton Böhm'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 Anton Böhm with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anton Böhm more than expected).
Fields of papers citing papers by Anton Böhm
This network shows the impact of papers produced by Anton Böhm. 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 Anton Böhm. The network helps show where Anton Böhm may publish in the future.
Co-authors
The 23 scholars most cited alongside Anton Böhm, 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 | U-Net: deep learning for cell counting, detection, and morphometry Hit paper breakdown → | 2018 | 1447 |
| 2 | Gland segmentation in colon histology images: The glas challenge contest Hit paper breakdown → | 2016 | 763 |
| 3 | 2015 | 30 | |
| 4 | 2018 | 13 | |
| 5 | 2019 | 4 | |
| 6 | 2020 | 2 |
About Anton Böhm
Anton Böhm is a scholar working on Biophysics, Computer Vision and Pattern Recognition, Cell Biology, Artificial Intelligence and Nuclear Energy and Engineering, having authored 6 papers that have together received 2.3k indexed citations. Recurring topics across this work include Cell Image Analysis Techniques (3 papers), Advanced Neural Network Applications (3 papers), Medical Image Segmentation Techniques (2 papers), Hippo pathway signaling and YAP/TAZ (1 paper) and AI in cancer detection (1 paper). The work is most often cited by research in Biophysics (509 citations), Computer Vision and Pattern Recognition (727 citations), Health Informatics (57 citations), Artificial Intelligence (624 citations) and Structural Biology (27 citations). Anton Böhm has collaborated with scholars based in Germany, Switzerland and United Kingdom. Frequent co-authors include Olaf Ronneberger, Philipp Kainz, Martin Urschler, Xiaojuan Qi, Nasir Rajpoot, Josien P. W. Pluim, Bassem Ben Cheikh, David R. J. Snead, Michael Pfeiffer and Hao Chen. Their work appears in journals such as Nature Methods, eLife, Medical Image Analysis and IEEE International Symposium on Biomedical Imaging (ISBI).
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.