Michael Suehling

27 papers receiving 715 citations

Peers

Michael Suehling
Comparison fields: 5 of 66
  • Health Informatics 18
  • Radiology, Nuclear Medicine and Imaging 252
  • Computer Vision and Pattern Recognition 205
  • Oral Surgery 52
  • Biomedical Engineering 237
Replace Vladimír Pekar with:
Vladimír Pekar Germany
Michael Wels Germany
Vincent Agnus France
Chui-Mei Tiu Taiwan
R Garberoglio Italy
Maurice Pradella Switzerland
Nikolas Leßmann Netherlands
Chengwen Chu Switzerland
Martin Segeroth Switzerland
Kyong Joon Lee South Korea
Michael Suehling relative to Vladimír Pekar Germany Vladimír Pekar's profile →
Citations per field
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Citations per year

Countries citing papers authored by Michael Suehling

Since Specialization
Citations

This map shows the geographic impact of Michael Suehling'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 Suehling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Suehling more than expected).

Fields of papers citing papers by Michael Suehling

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Suehling. 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 Suehling. The network helps show where Michael Suehling may publish in the future.

Co-authors

The 25 scholars most cited alongside Michael Suehling, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael Suehling Line = papers co-authored together Michael Suehling links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200895
2 201194
3 200991
4 201276
5 201163
6 201344
7 202042
8 201938
9 200932
10 201026
11 201326
12 201318
13 200815
14 201414
15 20099
16 20089
17 20128
18 20077
19 20126
20
Predicting Lesion Growth and Patient Survival in Colorectal Cancer Patients using Deep Neural Networks
20185

About Michael Suehling

Michael Suehling is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Biomedical Engineering, Artificial Intelligence and Pulmonary and Respiratory Medicine, having authored 28 papers that have together received 731 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (7 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), AI in cancer detection (7 papers), Medical Imaging and Analysis (6 papers), Aortic Disease and Treatment Approaches (3 papers), Medical Imaging Techniques and Applications (3 papers), Dental Radiography and Imaging (3 papers) and Colorectal Cancer Screening and Detection (3 papers). The work is most often cited by research in Health Informatics (18 citations), Radiology, Nuclear Medicine and Imaging (252 citations), Computer Vision and Pattern Recognition (205 citations), Oral Surgery (52 citations) and Biomedical Engineering (237 citations). Michael Suehling has collaborated with scholars based in Germany, United States and Italy. Frequent co-authors include Dorin Comaniciu, Michael Wels, Bogdan Georgescu, Yefeng Zheng, S. Kevin Zhou, Adrian Barbu, Sascha Seifert, David Liu, Alexander Cavallaro and Haibin Ling. Their work appears in journals such as RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, European Radiology, Investigative Radiology, Medical Image Analysis and Journal of Thoracic Imaging.

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.

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