Måns Larsson

979 citations
34 papers · 630 · h-index 13

Impact in

Papers in

Måns Larsson

31 papers receiving 604 citations

Peers

Måns Larsson
Comparison fields: 5 of 91
  • Computer Vision and Pattern Recognition 369
  • Health Informatics 13
  • Signal Processing 88
  • Radiology, Nuclear Medicine and Imaging 117
  • Media Technology 32
Replace Chaowei Fang with:
Chaowei Fang China
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Huaici Zhao China
Michalis A. Savelonas Greece
Tohru Kamiya Japan
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Shuchao Pang China
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Citations per field
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Citations per year

Countries citing papers authored by Måns Larsson

Since Specialization
Citations

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

Fields of papers citing papers by Måns Larsson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Måns Larsson, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Måns Larsson Line = papers co-authored together Måns Larsson links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2000169
2 201888
3 201838
4 200334
5 202129
6 200029
7 199927
8 202425
9 202023
10 201023
11 201917
12 201716
13 202113
14 202312
15 202212
16 202112
17 20249
18 20219
19
Deepseg: Abdominal Organ Segmentation Using Deep Convolutional Neural Networks
20166
20 20226

About Måns Larsson

Måns Larsson is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Pulmonary and Respiratory Medicine, Signal Processing and Biomedical Engineering, having authored 34 papers that have together received 630 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (8 papers), Medical Image Segmentation Techniques (4 papers), Domain Adaptation and Few-Shot Learning (4 papers), Digital Filter Design and Implementation (4 papers), Advanced Data Compression Techniques (4 papers), Geophysical and Geoelectrical Methods (3 papers), Image and Signal Denoising Methods (3 papers) and Medical Imaging and Analysis (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (369 citations), Health Informatics (13 citations), Signal Processing (88 citations), Radiology, Nuclear Medicine and Imaging (117 citations) and Media Technology (32 citations). Måns Larsson has collaborated with scholars based in Sweden, Denmark and United Kingdom. Frequent co-authors include C. Christopoulos, Fredrik Kahl, Yuhang Zhang, Daniel Förnvik, Lars Edenbrandt, Shuai Zheng, Anurag Arnab, Philip H. S. Torr, Olof Enqvist and Bernardino Romera‐Paredes. Their work appears in journals such as Clinical Physiology and Functional Imaging, European Radiology, Journal of Nuclear Cardiology, Scientific Reports and The International Journal of Cardiovascular Imaging.

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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