Alexander Enk

65 papers receiving 2.0k citations

Alexander Enk's Hit Papers

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review 2018 · 296 citations
2960+2+5Years since publication50100150200250

Peers

Alexander Enk
Comparison fields: 5 of 148
  • Health Informatics 127
  • Oncology 1.1k
  • Dermatology 334
  • Biophysics 145
  • Artificial Intelligence 799
Replace D.M. Jukic with:
D.M. Jukic United States
Achim Hekler Germany
Holger A. Haenssle Germany
Myoung Shin Kim South Korea
Titus J. Brinker Germany
Gyeong‐Hun Park South Korea
Christine Fink Germany
Michael A. Marchetti United States
John H. Sinard United States
Andreas Blum Germany
Alexander Enk relative to D.M. Jukic United States D.M. Jukic's profile →
Citations per field
00.5×
D.M. Jukic · 1×
Citations per year

Countries citing papers authored by Alexander Enk

Since Specialization
Citations

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

Fields of papers citing papers by Alexander Enk

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 77 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review
Hit paper breakdown →
2018296
2 2019215
3 2019198
4 2019184
5 2019138
6 201194
7 201980
8 202077
9 200969
10 201048
11 200942
12 201841
13 201637
14 200835
15 200633
16 201330
17 201927
18 201826
19 201126
20 201925

About Alexander Enk

Alexander Enk is a scholar working on Oncology, Epidemiology, Dermatology, Immunology and Pulmonary and Respiratory Medicine, having authored 77 papers that have together received 2.0k indexed citations. Recurring topics across this work include Cutaneous Melanoma Detection and Management (24 papers), Nonmelanoma Skin Cancer Studies (23 papers), AI in cancer detection (10 papers), Photodynamic Therapy Research Studies (8 papers), Immunotherapy and Immune Responses (8 papers), Skin Protection and Aging (7 papers), Cancer Immunotherapy and Biomarkers (6 papers) and Cutaneous lymphoproliferative disorders research (6 papers). The work is most often cited by research in Health Informatics (127 citations), Oncology (1.1k citations), Dermatology (334 citations), Biophysics (145 citations) and Artificial Intelligence (799 citations). Alexander Enk has collaborated with scholars based in Germany, United States and Austria. Frequent co-authors include Christof von Kalle, Titus J. Brinker, Achim Hekler, Dirk Schadendorf, Joachim Klode, Jochen Utikal, Carola Berking, Patrick Gholam, Stefan Fröhling and Christine Fink. Their work appears in journals such as JDDG Journal der Deutschen Dermatologischen Gesellschaft, European Journal of Cancer, Journal of Medical Internet Research, Acta Dermato Venereologica and Journal of the European Academy of Dermatology and Venereology.

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.

Explore authors with similar magnitude of impact