Andrzej Skalski
- Artificial Intelligence top 10%
- Physiology
- Computer Vision and Pattern Recognition top 10%
- Biomedical Engineering
- Signal Processing top 5%
- Co-authors
- Marek WodzińskiDaria HemmerlingJanusz GajdaJuan Rafael Orozco‐ArroyaveElmar NöthTomasz P. ZielińskiTomasz DrewniakPaweł Turcza
- Topics
- Medical Image Segmentation Techniques (22 papers)Anatomy and Medical Technology (11 papers)AI in cancer detection (11 papers)
- Partner nations
- PolandUnited StatesUnited Kingdom
In The Last Decade
Andrzej Skalski
56 papers receiving 500 citations
Peers
Comparison fields: 5 of 71
- Artificial Intelligence 176
- Physiology 161
- Computer Vision and Pattern Recognition 141
- Biomedical Engineering 121
- Signal Processing 103
Countries citing papers authored by Andrzej Skalski
This map shows the geographic impact of Andrzej Skalski'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 Andrzej Skalski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrzej Skalski more than expected).
Fields of papers citing papers by Andrzej Skalski
This network shows the impact of papers produced by Andrzej Skalski. 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 Andrzej Skalski. The network helps show where Andrzej Skalski may publish in the future.
Co-authorship network of co-authors of Andrzej Skalski
This figure shows the co-authorship network connecting the top 25 collaborators of Andrzej Skalski. A scholar is included among the top collaborators of Andrzej Skalski 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 Andrzej Skalski. Andrzej Skalski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 4 | |
| 6 | 4 | |
| 7 | 6 | |
| 8 | 18 | |
| 9 | 12 | |
| 10 | 11 | |
| 11 | 7 | |
| 12 | 3 | |
| 13 | 8 | |
| 14 | 15 | |
| 15 | 39 | |
| 16 | 0 | |
| 17 | Comparison of ASM and AAM-based segmentation of prostate image in the CT scans for radiotherapy planning | 4 |
| 18 | Aplikacja wspierająca proces konturowania organów w danych medycznych | 0 |
| 19 | Modelowanie struktur anatomicznych dla potrzeb planowania leczenia w procesie radioterapii nowotworu prostaty | 0 |
| 20 | Segmentacja i dopasowywanie cyfrowych obrazów medycznych: przetwarzanie nagrań wideo-endoskopowych strun głosowych oraz danych tomograficznych zmian rakowych | 1 |
About Andrzej Skalski
Andrzej Skalski is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Radiation, having authored 61 papers that have together received 523 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (22 papers), Anatomy and Medical Technology (11 papers) and AI in cancer detection (11 papers). The work is most often cited by research in Signal Processing (103 citations), Computer Vision and Pattern Recognition (141 citations) and Gastroenterology (37 citations). Andrzej Skalski has collaborated with scholars based in Poland, United States and United Kingdom. Frequent co-authors include Marek Wodziński, Daria Hemmerling, Janusz Gajda, Juan Rafael Orozco‐Arroyave, Elmar Nöth, Tomasz P. Zieliński, Tomasz Drewniak, Paweł Turcza, Jacek Jakubowski and Piotr Kędzierawski. Their work appears in journals such as Sensors, Physics in Medicine and Biology and Medical Physics.
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.