Heinrich Schulz
- Radiology, Nuclear Medicine and Imaging top 10%
- Biomedical Engineering
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Surgery
- Co-authors
- Bradford J. WoodSheng XuJochen KrückerNeil GlossopAnand ViswanathanJörn BorgertDmitry V. DylovIrina Fedulova
- Topics
- Medical Image Segmentation Techniques (7 papers)Radiomics and Machine Learning in Medical Imaging (6 papers)Medical Imaging Techniques and Applications (6 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE AccessSensors
- Partner nations
- GermanyNetherlandsUnited States
In The Last Decade
Heinrich Schulz
18 papers receiving 312 citations
Peers
Comparison fields: 5 of 75
- Radiology, Nuclear Medicine and Imaging 134
- Biomedical Engineering 103
- Computer Vision and Pattern Recognition 77
- Artificial Intelligence 76
- Surgery 57
Countries citing papers authored by Heinrich Schulz
This map shows the geographic impact of Heinrich Schulz'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 Heinrich Schulz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heinrich Schulz more than expected).
Fields of papers citing papers by Heinrich Schulz
This network shows the impact of papers produced by Heinrich Schulz. 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 Heinrich Schulz. The network helps show where Heinrich Schulz may publish in the future.
Co-authorship network of co-authors of Heinrich Schulz
This figure shows the co-authorship network connecting the top 25 collaborators of Heinrich Schulz. A scholar is included among the top collaborators of Heinrich Schulz 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 Heinrich Schulz. Heinrich Schulz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 14 | |
| 3 | 80 | |
| 4 | 5 | |
| 5 | 0 | |
| 6 | 6 | |
| 7 | 1 | |
| 8 | Comparison of deep learning-based techniques for organ segmentation in abdominal CT images | 6 |
| 9 | 0 | |
| 10 | 1 | |
| 11 | 7 | |
| 12 | 5 | |
| 13 | 4 | |
| 14 | 1 | |
| 15 | 1 | |
| 16 | 7 | |
| 17 | 161 | |
| 18 | 1 | |
| 19 | 12 | |
| 20 | 11 |
About Heinrich Schulz
Heinrich Schulz is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 20 papers that have together received 326 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Medical Imaging Techniques and Applications (6 papers). The work is most often cited by research in Health Informatics (11 citations), Radiation (57 citations) and Radiology, Nuclear Medicine and Imaging (134 citations). Heinrich Schulz has collaborated with scholars based in Germany, Netherlands and United States. Frequent co-authors include Bradford J. Wood, Sheng Xu, Jochen Krücker, Neil Glossop, Anand Viswanathan, Jörn Borgert, Dmitry V. Dylov, Irina Fedulova, Bart Bakker and Steffen Renisch. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Sensors.
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.