Daniel Heim

17 papers receiving 560 citations

Peers

Daniel Heim
Comparison fields: 5 of 91
  • Biophysics 115
  • Health Informatics 23
  • Artificial Intelligence 253
  • Computer Vision and Pattern Recognition 159
  • Radiology, Nuclear Medicine and Imaging 142
Replace Quirine F. Manson with:
Quirine F. Manson Netherlands
Bassem Ben Cheikh France
Douglas Bowman United States
Marcory van Dijk Netherlands
Kimmo Kartasalo Finland
Oscar Geessink Netherlands
Marcial García‐Rojo Spain
Mart van Rijthoven Netherlands
Helen Bardwell United Kingdom
Nikolas Stathonikos Netherlands
Daniel Heim relative to Quirine F. Manson Netherlands Quirine F. Manson's profile →
Citations per field
00.5×10×16.7×
Quirine F. Manson · 1×
Citations per year

Countries citing papers authored by Daniel Heim

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Heim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

17 of 17 papers shown
#Work
1 2012170
2 2021103
3 201978
4 201546
5 201634
6 201430
7 201329
8 202120
9 202220
10 201610
11 20159
12 20198
13 20187
14 20186
15 20203
16 20193
17 20182

About Daniel Heim

Daniel Heim is a scholar working on Cancer Research, Biophysics, Pulmonary and Respiratory Medicine, Pathology and Forensic Medicine and Computer Vision and Pattern Recognition, having authored 17 papers that have together received 578 indexed citations. Recurring topics across this work include Cancer Genomics and Diagnostics (11 papers), Lung Cancer Treatments and Mutations (6 papers), Cell Image Analysis Techniques (4 papers), AI in cancer detection (4 papers), Genetic factors in colorectal cancer (3 papers), Epigenetics and DNA Methylation (2 papers), Bioinformatics and Genomic Networks (2 papers) and Molecular Biology Techniques and Applications (2 papers). The work is most often cited by research in Biophysics (115 citations), Health Informatics (23 citations), Artificial Intelligence (253 citations), Computer Vision and Pattern Recognition (159 citations) and Radiology, Nuclear Medicine and Imaging (142 citations). Daniel Heim has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Frederick Klauschen, Stephan Wienert, Albrecht Stenzinger, Carsten Denkert, Peter Hufnagl, Manfred Dietel, Michael Beil, Kai Saeger, Michael Bockmayr and Klaus‐Robert Müller. Their work appears in journals such as International Journal of Cancer, Scientific Reports, Histopathology, Diagnostic Pathology and Nature Machine Intelligence.

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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