Marek Śmieja
- Cancer Research top 5%
- Cardiology and Cardiovascular Medicine top 10%
- Surgery
- Molecular Biology
- Hematology top 10%
- Co-authors
- François CambienOdette PoirierGerd HäfnerHans J. RupprechtJürgen MeyerChristoph BickelLaurence TiretStefan Blankenberg
- Topics
- Face and Expression Recognition (6 papers)Domain Adaptation and Few-Shot Learning (6 papers)Neural Networks and Applications (6 papers)
- Partner nations
- PolandCanadaUnited Kingdom
In The Last Decade
Marek Śmieja
33 papers receiving 819 citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Cancer Research 332
- Cardiology and Cardiovascular Medicine 201
- Surgery 144
- Molecular Biology 140
- Hematology 136
Countries citing papers authored by Marek Śmieja
This map shows the geographic impact of Marek Śmieja'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 Marek Śmieja with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marek Śmieja more than expected).
Fields of papers citing papers by Marek Śmieja
This network shows the impact of papers produced by Marek Śmieja. 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 Marek Śmieja. The network helps show where Marek Śmieja may publish in the future.
Co-authorship network of co-authors of Marek Śmieja
This figure shows the co-authorship network connecting the top 25 collaborators of Marek Śmieja. A scholar is included among the top collaborators of Marek Śmieja 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 Marek Śmieja. Marek Śmieja 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 | 2 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 4 | |
| 6 | 10 | |
| 7 | Can auto-encoders help with filling missing data? | 2 |
| 8 | Geometric Graph Convolutional Neural Networks. | 5 |
| 9 | Set aggregation network for structured data processing | 0 |
| 10 | Processing of missing data by neural networks | 4 |
| 11 | 15 | |
| 12 | 2 | |
| 13 | 17 | |
| 14 | Incomplete data representation for SVM classification. | 2 |
| 15 | 6 | |
| 16 | 13 | |
| 17 | 2 | |
| 18 | 8 | |
| 19 | 1 | |
| 20 | Plasma Concentrations and Genetic Variation of Matrix Metalloproteinase 9 and Prognosis of Patients With Cardiovascular Diseasebreakdown → | 616 |
About Marek Śmieja
Marek Śmieja is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biophysics, having authored 37 papers that have together received 839 indexed citations. Recurring topics across this work include Face and Expression Recognition (6 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Neural Networks and Applications (6 papers). The work is most often cited by research in Cancer Research (332 citations), Hematology (136 citations) and Cardiology and Cardiovascular Medicine (201 citations). Marek Śmieja has collaborated with scholars based in Poland, Canada and United Kingdom. Frequent co-authors include François Cambien, Odette Poirier, Gerd Häfner, Hans J. Rupprecht, Jürgen Meyer, Christoph Bickel, Laurence Tiret, Stefan Blankenberg, Jacek Tabor and Łukasz Struski. Their work appears in journals such as Circulation, PLoS ONE and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.