Michael Götz

7.0k total citations · 2 hit papers
30 papers, 1.7k citations indexed

About

Michael Götz is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Genetics. According to data from OpenAlex, Michael Götz has authored 30 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Radiology, Nuclear Medicine and Imaging, 9 papers in Artificial Intelligence and 6 papers in Genetics. Recurrent topics in Michael Götz's work include Radiomics and Machine Learning in Medical Imaging (16 papers), AI in cancer detection (6 papers) and Glioma Diagnosis and Treatment (5 papers). Michael Götz is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (16 papers), AI in cancer detection (6 papers) and Glioma Diagnosis and Treatment (5 papers). Michael Götz collaborates with scholars based in Germany, United States and Slovakia. Michael Götz's co-authors include Klaus Maier‐Hein, David Bonekamp, Philipp Kickingereder, Jian‐Yong Zhu, Elias Lazarides, Lawrence R. Koupal, Conor Woods, Otto D. Hensens, Jerrold M. Liesch and Willi Schmid and has published in prestigious journals such as Stroke, Scientific Reports and Radiology.

In The Last Decade

Michael Götz

26 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Epothilones, a new class of microtubule-stabilizing agents with a taxol-like mechanism of action.

1995 535 citations Michael Götz et al. profile →
Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models

2016 321 citations Philipp Kickingereder, Sina Burth et al. Radiology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michael Götz Germany	12	858	459	336	304	233	30	1.7k
Elizabeth J. Sutton United States	28	1.8k 2.1×	332 0.7×	327 1.0×	173 0.6×	284 1.2×	97	2.8k
John DeGroot United States	15	1.3k 1.6×	345 0.8×	848 2.5×	2.6k 8.5×	562 2.4×	32	3.5k
Roel Funke United States	24	232 0.3×	3.5k 7.6×	1.4k 4.1×	158 0.5×	1.0k 4.4×	43	5.0k
Lee B. Jordan United Kingdom	29	1.3k 1.5×	1.3k 2.7×	341 1.0×	38 0.1×	1.1k 4.9×	79	3.7k
Yu Kuang China	19	688 0.8×	166 0.4×	218 0.6×	62 0.2×	86 0.4×	77	1.2k
Gist H. Farr United States	32	144 0.2×	1.3k 2.8×	620 1.8×	175 0.6×	2.1k 8.9×	70	4.9k
Shen Zhao China	27	219 0.3×	1.1k 2.4×	919 2.7×	66 0.2×	561 2.4×	146	2.3k
Stephan Kruck Germany	28	539 0.6×	530 1.2×	1.4k 4.1×	45 0.1×	767 3.3×	120	2.8k
Ying Hou China	17	1.4k 1.6×	229 0.5×	837 2.5×	54 0.2×	94 0.4×	54	1.8k
David G. McFadden United States	21	183 0.2×	582 1.3×	335 1.0×	40 0.1×	2.0k 8.6×	36	3.6k

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-authorship network of co-authors of Michael Götz

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Götz. A scholar is included among the top collaborators of Michael Götz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael Götz. Michael Götz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Fischer, Maximilian, Peter Neher, Peter J. Schüffler, et al.. (2025). Unlocking the potential of digital pathology: Novel baselines for compression. Journal of Pathology Informatics. 17. 100421–100421.

Zengerling, Friedemann, et al.. (2025). Machine learning-based radiomics for bladder cancer staging: evaluating the role of imaging timing in differentiating T2 from T3 disease. Frontiers in Oncology. 15. 1591742–1591742.

Wolf, Daniel, et al.. (2024). Less is More: Selective reduction of CT data for self-supervised pre-training of deep learning models with contrastive learning improves downstream classification performance. Computers in Biology and Medicine. 183. 109242–109242. 1 indexed citations

Kannengießer, Stephan, Arthur Wunderlich, Xiaodong Zhong, et al.. (2024). Liver R2* with magnitude fitting in iron-overload patients – initial results on agreement between protocol settings and between 1.5T and 3T. Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition. 1 indexed citations

Wunderlich, Arthur, Holger Cario, Stephan Kannengießer, et al.. (2023). Segmental quantification of hepatic lipid content based on volumetric MRI data in patients with suspected iron overload. RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren. 196(5). 463–470. 1 indexed citations

Ropinski, Timo, et al.. (2023). Artificial intelligence in radiology – beyond the black box. RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren. 195(9). 797–803. 7 indexed citations

Baeßler, Bettina, Michael Götz, Charalambos Antoniades, et al.. (2023). Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective. Frontiers in Cardiovascular Medicine. 10. 1120361–1120361. 29 indexed citations

Wolf, Daniel, Sebastian Regnery, R. Tarnawski, et al.. (2022). Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation. Applied Sciences. 12(21). 10763–10763. 7 indexed citations

Wolf, Daniel, Stefan Schmidt, Wolfgang Thaiss, et al.. (2022). Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL). OPen Access Repositorium der Universität Ulm (OPARU) (Ulm University). 4 indexed citations

10.

Klein, André, Jiří Chmelík, Lukas T. Rotkopf, et al.. (2021). P-018: Automatic analysis of magnetic resonance imaging in multiple myeloma patients: deep-learning based pelvic bone marrow segmentation and radiomics analysis for prediction of plasma cell infiltration. Clinical Lymphoma Myeloma & Leukemia. 21. S49–S49. 3 indexed citations

11.

Götz, Michael & Klaus Maier‐Hein. (2020). Optimal Statistical Incorporation of Independent Feature Stability Information into Radiomics Studies. Scientific Reports. 10(1). 737–737. 17 indexed citations

12.

Götz, Michael, Marco Nolden, & Klaus Maier‐Hein. (2018). MITK Phenotyping: An open-source toolchain for image-based personalized medicine with radiomics. Radiotherapy and Oncology. 131. 108–111. 29 indexed citations

13.

Bonekamp, David, Simon Köhl, Manuel Wiesenfarth, et al.. (2018). Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values. Radiology. 289(1). 128–137. 156 indexed citations

14.

Kickingereder, Philipp, Michael Götz, John Muschelli, et al.. (2016). Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response. Clinical Cancer Research. 22(23). 5765–5771. 206 indexed citations

15.

Kickingereder, Philipp, Sina Burth, Antje Wick, et al.. (2016). Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models. Radiology. 280(3). 880–889. 321 indexed citations breakdown →

16.

Kickingereder, Philipp, Michael Götz, Antje Wick, et al.. (2016). OS4.6 Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Neuro-Oncology. 18(suppl_4). iv10–iv10. 2 indexed citations

17.

Maleshkova, Maria, Darko Katić, Christian Weber, et al.. (2015). Toward cognitive pipelines of medical assistance algorithms. International Journal of Computer Assisted Radiology and Surgery. 11(9). 1743–1753. 6 indexed citations

18.

Maleshkova, Maria, Michael Götz, Christian Weber, et al.. (2014). Using linked data and web APIs for automating the pre-processing of medical images. International Semantic Web Conference. 25–36. 3 indexed citations

19.

Schütz, Jean-Philippe, Michael Götz, Willi Schmid, & Daniel Mandallaz. (2006). Vulnerability of spruce (Picea abies) and beech (Fagus sylvatica) forest stands to storms and consequences for silviculture. European Journal of Forest Research. 125(3). 291–302. 190 indexed citations

20.

Wendt, Hartmut & Michael Götz. (1997). Brennstoffzellentechnik. Chemie in unserer Zeit. 31(6). 301–309. 6 indexed citations

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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