Maximilian Baust
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Biomedical Engineering
- Computational Mechanics top 10%
- Co-authors
- Nassir NavabSlobodan IlićMiroslava SlavchevaTingying PengKatja SteigerIréne EspositoAbhishek VahadaneAmit Sethi
- Topics
- Medical Image Segmentation Techniques (17 papers)3D Shape Modeling and Analysis (6 papers)Advanced Vision and Imaging (4 papers)
- Cited by
- Health InformaticsComputer Vision and Pattern RecognitionComputer Graphics and Computer-Aided Design
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceScientific ReportsIEEE Transactions on Image Processing
- Partner nations
- GermanyUnited StatesIndia
In The Last Decade
Maximilian Baust
33 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 108
- Artificial Intelligence 743
- Computer Vision and Pattern Recognition 637
- Radiology, Nuclear Medicine and Imaging 499
- Biomedical Engineering 186
- Computational Mechanics 127
Countries citing papers authored by Maximilian Baust
This map shows the geographic impact of Maximilian Baust'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 Maximilian Baust with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maximilian Baust more than expected).
Fields of papers citing papers by Maximilian Baust
This network shows the impact of papers produced by Maximilian Baust. 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 Maximilian Baust. The network helps show where Maximilian Baust may publish in the future.
Co-authorship network of co-authors of Maximilian Baust
This figure shows the co-authorship network connecting the top 25 collaborators of Maximilian Baust. A scholar is included among the top collaborators of Maximilian Baust 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 Maximilian Baust. Maximilian Baust is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 18 | |
| 2 | 10 | |
| 3 | 40 | |
| 4 | 294 | |
| 5 | 23 | |
| 6 | 71 | |
| 7 | 133 | |
| 8 | 110 | |
| 9 | 81 | |
| 10 | 32 | |
| 11 | 2 | |
| 12 | 1 | |
| 13 | 60 | |
| 14 | 5 | |
| 15 | 1 | |
| 16 | 9 | |
| 17 | 19 | |
| 18 | 14 | |
| 19 | 16 | |
| 20 | 6 |
About Maximilian Baust
Maximilian Baust is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 33 papers that have together received 1.5k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (17 papers), 3D Shape Modeling and Analysis (6 papers) and Advanced Vision and Imaging (4 papers). The work is most often cited by research in Health Informatics (55 citations), Computer Vision and Pattern Recognition (637 citations) and Computer Graphics and Computer-Aided Design (84 citations). Maximilian Baust has collaborated with scholars based in Germany, United States and India. Frequent co-authors include Nassir Navab, Slobodan Ilić, Miroslava Slavcheva, Tingying Peng, Katja Steiger, Iréne Esposito, Abhishek Vahadane, Amit Sethi, Anna Melissa Schlitter and Shadi Albarqouni. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and IEEE Transactions on Image Processing.
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.