Stephan Dreiseitl

3.6k citations
41 papers · 2.6k indexed · 1 hit paper · h-index 15
Topics
Cutaneous Melanoma Detection and Management (9 papers)AI in cancer detection (8 papers)Data Mining Algorithms and Applications (4 papers)

In The Last Decade

Stephan Dreiseitl

37 papers receiving 2.5k citations

Hit Papers

Logistic regression and artificial neural network classif...2002202620102018200250010001.5k

Peers

Stephan Dreiseitl
Comparison fields: 5 of 198
  • Artificial Intelligence 803
  • Radiology, Nuclear Medicine and Imaging 360
  • Oncology 301
  • Pulmonary and Respiratory Medicine 291
  • Epidemiology 247
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Paulo Lisböa United Kingdom
Constantin Aliferis United States
Takaya Saito Norway
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Citations per field
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Citations per year

Countries citing papers authored by Stephan Dreiseitl

Since Specialization
Citations

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

Fields of papers citing papers by Stephan Dreiseitl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stephan Dreiseitl

This figure shows the co-authorship network connecting the top 25 collaborators of Stephan Dreiseitl. A scholar is included among the top collaborators of Stephan Dreiseitl 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 Stephan Dreiseitl. Stephan Dreiseitl 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 25
2 6
3
Prediction of Metastatic Events in Patients With Cutaneous Melanoma
1
4 29
5 16
6 2
7 48
8 13
9
Improving calibration of logistic regression models by local estimates.
3
10
Applying a decision support system in clinical practice: results from melanoma diagnosis.
7
11 10
12 70
13
Logistic regression and artificial neural network classification models: a methodology reviewbreakdown →
1594
14 1
15 4
16 1
17
A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions
0
18 191
19 121
20 47

About Stephan Dreiseitl

Stephan Dreiseitl is a scholar working on Biophysics, Artificial Intelligence and Statistics and Probability, having authored 41 papers that have together received 2.6k indexed citations. Recurring topics across this work include Cutaneous Melanoma Detection and Management (9 papers), AI in cancer detection (8 papers) and Data Mining Algorithms and Applications (4 papers). The work is most often cited by research in Health Information Management (215 citations), Health Informatics (48 citations) and Artificial Intelligence (803 citations). Stephan Dreiseitl has collaborated with scholars based in Austria, United States and Antigua and Barbuda. Frequent co-authors include Lucila Ohno‐Machado, Michael Binder, Harald Kittler, Staal A. Vinterbo, Michael Binder, Holger Billhardt, Graham E. Quinn, Gil Binenbaum, Gui‐Shuang Ying and Melanie Osl. Their work appears in journals such as Bioinformatics, PEDIATRICS and BMC Bioinformatics.

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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