Johannes Feulner
- Computer Vision and Pattern Recognition top 10%
- Radiology, Nuclear Medicine and Imaging
- Artificial Intelligence
- Signal Processing top 10%
- Biomedical Engineering
- Co-authors
- Dorin ComaniciuS. Kevin ZhouJoachim HorneggerAlexander CavallaroSascha SeifertWolfram MenzelHermann HildMartin Huber
- Topics
- Medical Image Segmentation Techniques (5 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)Advanced Neural Network Applications (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionSignal ProcessingRadiology, Nuclear Medicine and Imaging
- Journals
- Medical Image AnalysisLecture notes in computer scienceComputerized Medical Imaging and Graphics
- Partner nations
- GermanyUnited States
In The Last Decade
Johannes Feulner
12 papers receiving 228 citations
Peers
Comparison fields: 5 of 49
- Computer Vision and Pattern Recognition 140
- Radiology, Nuclear Medicine and Imaging 108
- Artificial Intelligence 72
- Signal Processing 50
- Biomedical Engineering 48
Countries citing papers authored by Johannes Feulner
This map shows the geographic impact of Johannes Feulner'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 Johannes Feulner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Johannes Feulner more than expected).
Fields of papers citing papers by Johannes Feulner
This network shows the impact of papers produced by Johannes Feulner. 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 Johannes Feulner. The network helps show where Johannes Feulner may publish in the future.
Co-authorship network of co-authors of Johannes Feulner
This figure shows the co-authorship network connecting the top 25 collaborators of Johannes Feulner. A scholar is included among the top collaborators of Johannes Feulner 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 Johannes Feulner. Johannes Feulner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 44 | |
| 2 | 9 | |
| 3 | 13 | |
| 4 | 4 | |
| 5 | 15 | |
| 6 | 91 | |
| 7 | 10 | |
| 8 | 5 | |
| 9 | 1 | |
| 10 | Melonet: Neural Networks that Learn Harmony-Based Melodic Variations | 6 |
| 11 | Neural Networks that Learn and Reproduce Various Styles of Harmonization | 7 |
| 12 | HARMONET: A Neural Net for Harmonizing Chorales in the Style of J. S. Bach | 41 |
About Johannes Feulner
Johannes Feulner is a scholar working on Computer Vision and Pattern Recognition, Instrumentation and Radiology, Nuclear Medicine and Imaging, having authored 12 papers that have together received 246 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Advanced Neural Network Applications (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (140 citations), Signal Processing (50 citations) and Radiology, Nuclear Medicine and Imaging (108 citations). Johannes Feulner has collaborated with scholars based in Germany and United States. Frequent co-authors include Dorin Comaniciu, S. Kevin Zhou, Joachim Hornegger, Alexander Cavallaro, Sascha Seifert, Wolfram Menzel, Hermann Hild, Martin Huber, Adrian Barbu and David Liu. Their work appears in journals such as Medical Image Analysis, Lecture notes in computer science and Computerized Medical Imaging and Graphics.
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.