Patrick Bilic
Impact in
-
- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Neurology top 2%
- Brain Tumor Detection and Classification
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 3
-
- Advanced Neural Network Applications 3
- Co-authors
- Seyed‐Ahmad Ahmadi (1 shared paper)Melvin D’Anastasi (1 shared paper)Marco Armbruster (1 shared paper)Markus Rempfler (1 shared paper)Marc Bickel (1 shared paper)Bjoern H. Menze (1 shared paper)Wieland H. Sommer (1 shared paper)Felix Hofmann (1 shared paper)
- Journals
- Medical Image Analysis (1 paper)Nature Communications (1 paper)Lecture notes in computer science (1 paper)
- Partner nations
- GermanyHong KongSwitzerland
In The Last Decade
Patrick Bilic
3 papers receiving 2.0k citations
Patrick Bilic's Hit Papers
Peers
Comparison fields: 5 of 103
- Computer Vision and Pattern Recognition 975
- Neurology 333
- Radiology, Nuclear Medicine and Imaging 881
- Health Informatics 54
- Artificial Intelligence 737
Countries citing papers authored by Patrick Bilic
This map shows the geographic impact of Patrick Bilic'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 Patrick Bilic with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Patrick Bilic more than expected).
Fields of papers citing papers by Patrick Bilic
This network shows the impact of papers produced by Patrick Bilic. 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 Patrick Bilic. The network helps show where Patrick Bilic may publish in the future.
Co-authors
The 9 scholars most cited alongside Patrick Bilic, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | The Medical Segmentation Decathlon Hit paper breakdown → | 2022 | 854 |
| 2 | The Liver Tumor Segmentation Benchmark (LiTS) Hit paper breakdown → | 2022 | 664 |
| 3 | Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields Hit paper breakdown → | 2016 | 499 |
About Patrick Bilic
Patrick Bilic is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Health Informatics, Artificial Intelligence and Insect Science, having authored 3 papers that have together received 2.0k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), AI in cancer detection (2 papers) and Artificial Intelligence in Healthcare and Education (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (975 citations), Neurology (333 citations), Radiology, Nuclear Medicine and Imaging (881 citations), Health Informatics (54 citations) and Artificial Intelligence (737 citations). Patrick Bilic has collaborated with scholars based in Germany, Hong Kong and Switzerland. Frequent co-authors include Seyed‐Ahmad Ahmadi, Melvin D’Anastasi, Marco Armbruster, Markus Rempfler, Marc Bickel, Bjoern H. Menze, Wieland H. Sommer, Felix Hofmann and Patrick Ferdinand Christ. Their work appears in journals such as Medical Image Analysis, Nature Communications and Lecture notes in computer science.
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.