Fritz Albregtsen

2.2k citations
61 papers · 1.4k indexed · 1 hit paper · h-index 21
Topics
Radiomics and Machine Learning in Medical Imaging (15 papers)Medical Image Segmentation Techniques (11 papers)AI in cancer detection (11 papers)
Journals
NatureThe LancetSHILAP Revista de lepidopterología

In The Last Decade

Fritz Albregtsen

61 papers receiving 1.3k citations

Hit Papers

Deep learning for prediction of colorectal cancer outcome...20202026202220242020100200300

Peers

Fritz Albregtsen
Comparison fields: 5 of 132
  • Artificial Intelligence 452
  • Radiology, Nuclear Medicine and Imaging 429
  • Computer Vision and Pattern Recognition 401
  • Oncology 238
  • Molecular Biology 179
Replace Jiawen Yao with:
Jiawen Yao China
Laura E. Boucheron United States
Michael Beil Germany
Yiqing Shen China
G. Russo Italy
Martin Halicek United States
Quan Chen United States
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Citations per field
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Citations per year

Countries citing papers authored by Fritz Albregtsen

Since Specialization
Citations

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

Fields of papers citing papers by Fritz Albregtsen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fritz Albregtsen

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 5
2 5
3 4
4 56
5 13
6 9
7 13
8 9
9 5
10 35
11 7
12
Evaluation of noise in dna fingerprint images produced by hybridization techniques
1
13 26
14 3
15 34
16 32
17 18
18 49
19
METHODS TO ESTIMATE AREAS AND PERIMETERS OF BLOB-LIKE OBJECTS: A COMPARISON
16
20
Texture Features from Gray level Gap Length Matrix.
16

About Fritz Albregtsen

Fritz Albregtsen is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Parasitology, having authored 61 papers that have together received 1.4k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (15 papers), Medical Image Segmentation Techniques (11 papers) and AI in cancer detection (11 papers). The work is most often cited by research in Health Informatics (37 citations), Computer Vision and Pattern Recognition (401 citations) and Radiology, Nuclear Medicine and Imaging (429 citations). Fritz Albregtsen has collaborated with scholars based in Norway, United Kingdom and South Africa. Frequent co-authors include Håvard E. Danielsen, Birgitte Nielsen, P. Maltby, Tarjei S. Hveem, Knut Liestøl, Andreas Kleppe, Manohar Pradhan, Arild Nesbakken, Neil A. Shepherd and Ian Tomlinson. Their work appears in journals such as Nature, The Lancet and SHILAP Revista de lepidopterología.

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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