Max Schmitt

15 papers receiving 537 citations

Peers

Max Schmitt
Comparison fields: 5 of 96
  • Health Informatics 74
  • Biophysics 62
  • Oncology 269
  • Artificial Intelligence 276
  • Radiology, Nuclear Medicine and Imaging 148
Replace Eva Krieghoff‐Henning with:
Eva Krieghoff‐Henning Germany
Stefan Fröhling Germany
Shunichi Jinnai Japan
Ferdinand Toberer Germany
Tanja Jutzi Germany
Roman C. Maron Germany
Richard Colling United Kingdom
Philipp Jansen Germany
Teresa Deinlein Austria
Jennifer DeFazio United States
Max Schmitt relative to Eva Krieghoff‐Henning Germany Eva Krieghoff‐Henning's profile →
Citations per field
00.5×1.5×
Eva Krieghoff‐Henning · 1×
Citations per year

Countries citing papers authored by Max Schmitt

Since Specialization
Citations

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

Fields of papers citing papers by Max Schmitt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

16 of 16 papers shown
#Work
1 2019184
2 2020106
3 202047
4 202146
5 202140
6 202028
7 202223
8
Measurement of plasminogen activator system components in plasma and tumor tissue extracts obtained from patients with breast cancer: an EORTC Receptor and Biomarker Group collaboration.
200523
9 200319
10 202313
11 202111
12 20085
13 19932
14
[Flow cytometry DNA analysis of pure cell nuclei from formalin fixed paraffin sections in primary breast cancer: correlation with other prognostic factors].
19911
15
Experimental Setup for Investigation and Evaluation of a Mapping and Localization System
20191
16 20200

About Max Schmitt

Max Schmitt is a scholar working on Oncology, Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Cancer Research, having authored 16 papers that have together received 549 indexed citations. Recurring topics across this work include AI in cancer detection (9 papers), Cutaneous Melanoma Detection and Management (6 papers), Protease and Inhibitor Mechanisms (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Peptidase Inhibition and Analysis (2 papers), Cell Image Analysis Techniques (2 papers), Renal cell carcinoma treatment (2 papers) and Digital Imaging for Blood Diseases (1 paper). The work is most often cited by research in Health Informatics (74 citations), Biophysics (62 citations), Oncology (269 citations), Artificial Intelligence (276 citations) and Radiology, Nuclear Medicine and Imaging (148 citations). Max Schmitt has collaborated with scholars based in Germany, United States and Netherlands. Frequent co-authors include Titus J. Brinker, Achim Hekler, Jochen Utikal, Stefan Fröhling, Christof von Kalle, Wiebke Sondermann, Eva Krieghoff‐Henning, Dirk Schadendorf, Roman C. Maron and Dieter Krahl. Their work appears in journals such as Journal of Medical Internet Research, European Journal of Cancer, Biological Chemistry, Frontiers in Medicine and JDDG Journal der Deutschen Dermatologischen Gesellschaft.

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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