Michael Wels

838 citations
32 papers · 513 indexed · h-index 15

Michael Wels

32 papers receiving 499 citations

Peers

Michael Wels
Comparison fields: 5 of 67
  • Health Informatics 33
  • Radiology, Nuclear Medicine and Imaging 250
  • Oral Surgery 47
  • Computer Vision and Pattern Recognition 125
  • Neurology 50
Replace Keewon Shin with:
Keewon Shin South Korea
Michael Suehling Germany
Avi Ben-Cohen Israel
Liyan Lin China
Ryoungwoo Jang South Korea
Laurent Massoptier Italy
Jie-Zhi Cheng Taiwan
Stephanie Leung Canada
Ge-Ge Wu China
Michael Wels relative to Keewon Shin South Korea Keewon Shin's profile →
Citations per field
00.5×10×15×20×23×
Keewon Shin · 1×
Citations per year

Countries citing papers authored by Michael Wels

Since Specialization
Citations

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

Fields of papers citing papers by Michael Wels

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Michael Wels, 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 Wels Line = papers co-authored together Michael Wels links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20242
2 20239
3 20236
4 202310
5 20221
6 202215
7 20229
8 202119
9 202122
10 202011
11 201924
12 201917
13 201318
14 201344
15 20123
16 201275
17 201115
18 200914
19 200851
20 200812

About Michael Wels

Michael Wels is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging, Gastroenterology, Neurology and Computer Vision and Pattern Recognition, having authored 32 papers that have together received 513 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (9 papers), Medical Image Segmentation Techniques (9 papers), Advanced X-ray and CT Imaging (8 papers), Medical Imaging and Analysis (7 papers), Cardiac Imaging and Diagnostics (6 papers), Gastric Cancer Management and Outcomes (5 papers), Medical Imaging Techniques and Applications (4 papers) and Brain Tumor Detection and Classification (4 papers). The work is most often cited by research in Health Informatics (33 citations), Radiology, Nuclear Medicine and Imaging (250 citations), Oral Surgery (47 citations), Computer Vision and Pattern Recognition (125 citations) and Neurology (50 citations). Michael Wels has collaborated with scholars based in Germany, United States and China. Frequent co-authors include Michael Suehling, Dorin Comaniciu, Joachim Hornegger, B. Michael Kelm, Yefeng Zheng, Martin Huber, Sascha Seifert, S. Kevin Zhou, Gustavo Carneiro and Gözde Ünal. Their work appears in journals such as Scientific Reports, Journal of Thoracic Imaging, European Radiology, Frontiers in Oncology and Radiology Artificial Intelligence.

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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