Michael Götz

26 papers receiving 1.7k citations

Hit Papers

Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models 2016 · 321 citations
3211995202620052015100200300400500

Peers

Michael Götz
Comparison fields: 5 of 110
  • Health Informatics 50
  • Radiology, Nuclear Medicine and Imaging 858
  • Genetics 304
  • Oncology 459
  • Cell Biology 205
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Youn Soo Lee South Korea
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Citations per year

Countries citing papers authored by Michael Götz

Since Specialization
Citations

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

Fields of papers citing papers by Michael Götz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Epothilones, a new class of microtubule-stabilizing agents with a taxol-like mechanism of action.
Hit paper breakdown →
1995535
2
Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models
Hit paper breakdown →
2016321
3 2016206
4 2006190
5 2018156
6 2017112
7 201662
8 202329
9 201829
10 202223
11 202017
12 201812
13 20227
14 20237
15 20156
16 19976
17 20205
18 20224
19
Using linked data and web APIs for automating the pre-processing of medical images
20143
20 20213

About Michael Götz

Michael Götz is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics, Genetics, Information Systems and Management and Artificial Intelligence, having authored 30 papers that have together received 1.7k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (16 papers), AI in cancer detection (6 papers), Glioma Diagnosis and Treatment (5 papers), MRI in cancer diagnosis (4 papers), Liver Disease Diagnosis and Treatment (3 papers), Sarcoma Diagnosis and Treatment (3 papers), Medical Imaging Techniques and Applications (2 papers) and Distributed and Parallel Computing Systems (2 papers). The work is most often cited by research in Health Informatics (50 citations), Radiology, Nuclear Medicine and Imaging (858 citations), Genetics (304 citations), Oncology (459 citations) and Cell Biology (205 citations). Michael Götz has collaborated with scholars based in Germany, United States and Slovakia. Frequent co-authors include Klaus Maier‐Hein, David Bonekamp, Philipp Kickingereder, Jerrold M. Liesch, Jian‐Yong Zhu, Elias Lazarides, Conor Woods, Lawrence R. Koupal, Otto D. Hensens and Daniel Mandallaz. Their work appears in journals such as RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, Investigative Radiology, Radiology, Clinical Cancer Research and Scientific Reports.

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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