Michael Majurski

630 citations
19 papers · 351 indexed · h-index 8
Topics
Cell Image Analysis Techniques (11 papers)Image Processing Techniques and Applications (6 papers)Advanced Neural Network Applications (4 papers)
Partner nations
United StatesPolandEgypt

In The Last Decade

Michael Majurski

15 papers receiving 339 citations

Peers

Michael Majurski
Comparison fields: 5 of 82
  • Biophysics 161
  • Computer Vision and Pattern Recognition 112
  • Molecular Biology 92
  • Media Technology 83
  • Biomedical Engineering 63
Replace Joe Chalfoun with:
Joe Chalfoun United States
Umesh Adiga United States
Hans Netten Netherlands
Jesse Berent United Kingdom
Jane Hung United States
Suvadip Mukherjee United States
Estibaliz Gómez‐de‐Mariscal Portugal
Nick Thomas United Kingdom
Luhong Jin China
Xiaofei Han China
Michael Majurski relative to Joe Chalfoun United States Joe Chalfoun's profile →
Citations per field
00.5×1.5×
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Citations per year

Countries citing papers authored by Michael Majurski

Since Specialization
Citations

This map shows the geographic impact of Michael Majurski'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 Michael Majurski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Majurski more than expected).

Fields of papers citing papers by Michael Majurski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Majurski. 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 Michael Majurski. The network helps show where Michael Majurski may publish in the future.

Co-authorship network of co-authors of Michael Majurski

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Majurski. A scholar is included among the top collaborators of Michael Majurski 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 Michael Majurski. Michael Majurski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
#WorkIndexed citations
1 1
2 1
3 1
4 2
5 7
6 10
7 50
8 0
9 115
10 0
11 29
12 4
13 1
14 35
15 1
16 36
17 3
18 7
19 48

About Michael Majurski

Michael Majurski is a scholar working on Biophysics, Media Technology and Computer Vision and Pattern Recognition, having authored 19 papers that have together received 351 indexed citations. Recurring topics across this work include Cell Image Analysis Techniques (11 papers), Image Processing Techniques and Applications (6 papers) and Advanced Neural Network Applications (4 papers). The work is most often cited by research in Biophysics (161 citations), Media Technology (83 citations) and Structural Biology (10 citations). Michael Majurski has collaborated with scholars based in United States, Poland and Egypt. Frequent co-authors include Joe Chalfoun, Peter Bajcsy, Kiran Bhadriraju, Mary Brady, Adele P. Peskin, Walid Keyrouz, Alden Dima, Carl G. Simon, Michael Halter and Christina H. Stuelten. Their work appears in journals such as Scientific Reports, BMC Bioinformatics and Computer.

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.

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