Isabella Ellinger

73 papers receiving 3.1k citations

Isabella Ellinger's Hit Papers

A Multi-Organ Nucleus Segmentation Challenge 2019 · 405 citations
4050+2+4Years since publication100200300400

Peers

Isabella Ellinger
Comparison fields: 5 of 132
  • Oncology 746
  • Obstetrics and Gynecology 213
  • Biophysics 157
  • Artificial Intelligence 715
  • Computer Vision and Pattern Recognition 394
Replace Haichun Yang with:
Haichun Yang United States
Vipul Chimanlal Chitalia United States
Zongfang Li China
Shao-Gang Wang China
Hiroyuki Nishiyama Japan
Xiangqian Guo China
Todd M. Brusko United States
M. Oberholzer Switzerland
Lana Xia Garmire United States
Zhenyu Zhang China
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Citations per field
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Citations per year

Countries citing papers authored by Isabella Ellinger

Since Specialization
Citations

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

Fields of papers citing papers by Isabella Ellinger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Isabella Ellinger, 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 Isabella Ellinger Line = papers co-authored together Isabella Ellinger links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 76 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A Multi-Organ Nucleus Segmentation Challenge
Hit paper breakdown →
2019405
2 2015245
3 2020239
4 2019213
5 2018209
6 2018207
7 2021155
8 1999106
9 202091
10 200488
11 202186
12 199971
13 201170
14 201552
15 200151
16 202243
17 199941
18 201638
19 200137
20 200933

About Isabella Ellinger

Isabella Ellinger is a scholar working on Cell Biology, Obstetrics and Gynecology, Immunology and Allergy, Oncology and Biophysics, having authored 76 papers that have together received 3.2k indexed citations. Recurring topics across this work include Cellular transport and secretion (14 papers), AI in cancer detection (11 papers), Pregnancy and preeclampsia studies (11 papers), Monoclonal and Polyclonal Antibodies Research (10 papers), Reproductive System and Pregnancy (9 papers), Drug Transport and Resistance Mechanisms (6 papers), Cell Image Analysis Techniques (6 papers) and Cutaneous Melanoma Detection and Management (5 papers). The work is most often cited by research in Oncology (746 citations), Obstetrics and Gynecology (213 citations), Biophysics (157 citations), Artificial Intelligence (715 citations) and Computer Vision and Pattern Recognition (394 citations). Isabella Ellinger has collaborated with scholars based in Austria, United Kingdom and Sweden. Frequent co-authors include Amirreza Mahbod, Rupert Ecker, Gerald Schaefer, Renate Fuchs, Chunliang Wang, Waranya Chatuphonprasert, Kanokwan Jarukamjorn, Walter Hunziker, Georg Dorffner and Peter Pietschmann. Their work appears in journals such as Placenta, International Journal of Molecular Sciences, Electrophoresis, IEEE Transactions on Medical Imaging and BioMed Research International.

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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