Mariusz Bajger
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging
- Biomedical Engineering
- Epidemiology
- Co-authors
- Gobert LeeFei MaMurk J. BottemaJohn SlavotinekAmos R. OmondiMartin CaonSimon A. WilliamsLimin Yu
- Topics
- AI in cancer detection (22 papers)Medical Image Segmentation Techniques (16 papers)Image Retrieval and Classification Techniques (10 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceRadiology, Nuclear Medicine and Imaging
- Journals
- SHILAP Revista de lepidopterologíaPattern RecognitionFuzzy Sets and Systems
In The Last Decade
Mariusz Bajger
32 papers receiving 293 citations
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 212
- Computer Vision and Pattern Recognition 180
- Radiology, Nuclear Medicine and Imaging 91
- Biomedical Engineering 34
- Epidemiology 31
Countries citing papers authored by Mariusz Bajger
This map shows the geographic impact of Mariusz Bajger'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 Mariusz Bajger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mariusz Bajger more than expected).
Fields of papers citing papers by Mariusz Bajger
This network shows the impact of papers produced by Mariusz Bajger. 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 Mariusz Bajger. The network helps show where Mariusz Bajger may publish in the future.
Co-authorship network of co-authors of Mariusz Bajger
This figure shows the co-authorship network connecting the top 25 collaborators of Mariusz Bajger. A scholar is included among the top collaborators of Mariusz Bajger 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 Mariusz Bajger. Mariusz Bajger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 11 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 8 | |
| 8 | 5 | |
| 9 | 5 | |
| 10 | 8 | |
| 11 | 22 | |
| 12 | 6 | |
| 13 | 8 | |
| 14 | 6 | |
| 15 | 13 | |
| 16 | 11 | |
| 17 | 58 | |
| 18 | 5 | |
| 19 | 2 | |
| 20 | 0 |
About Mariusz Bajger
Mariusz Bajger is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Algebra and Number Theory, having authored 38 papers that have together received 310 indexed citations. Recurring topics across this work include AI in cancer detection (22 papers), Medical Image Segmentation Techniques (16 papers) and Image Retrieval and Classification Techniques (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (180 citations), Artificial Intelligence (212 citations) and Radiology, Nuclear Medicine and Imaging (91 citations). Mariusz Bajger has collaborated with scholars based in Australia, Czechia and Poland. Frequent co-authors include Gobert Lee, Fei Ma, Murk J. Bottema, John Slavotinek, Amos R. Omondi, Martin Caon, Simon A. Williams, Limin Yu, Kevin Clark and Marc Agzarian. Their work appears in journals such as SHILAP Revista de lepidopterología, Pattern Recognition and Fuzzy Sets and Systems.
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.