Max Schmitt

1.2k total citations
16 papers, 549 citations indexed

About

Max Schmitt is a scholar working on Oncology, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Max Schmitt has authored 16 papers receiving a total of 549 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Oncology, 9 papers in Artificial Intelligence and 3 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Max Schmitt's work include AI in cancer detection (9 papers), Cutaneous Melanoma Detection and Management (6 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Max Schmitt is often cited by papers focused on AI in cancer detection (9 papers), Cutaneous Melanoma Detection and Management (6 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Max Schmitt collaborates with scholars based in Germany, Netherlands and Bulgaria. Max Schmitt's 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 and has published in prestigious journals such as PLoS ONE, European Journal of Cancer and Journal of Medical Internet Research.

In The Last Decade

Max Schmitt

15 papers receiving 537 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Max Schmitt Germany 11 276 269 148 87 74 16 549
Michael Riben United States 12 241 0.9× 120 0.4× 150 1.0× 75 0.9× 38 0.5× 25 669
Eva Krieghoff‐Henning Germany 17 270 1.0× 311 1.2× 231 1.6× 84 1.0× 97 1.3× 25 820
Giovanni Lujan United States 11 325 1.2× 111 0.4× 217 1.5× 48 0.6× 41 0.6× 20 584
Stefan Fröhling Germany 8 467 1.7× 500 1.9× 163 1.1× 168 1.9× 102 1.4× 10 793
Richard Colling United Kingdom 18 318 1.2× 252 0.9× 263 1.8× 42 0.5× 97 1.3× 47 769
Shunichi Jinnai Japan 10 158 0.6× 245 0.9× 96 0.6× 79 0.9× 47 0.6× 21 455
Ole-Johan Skrede United Kingdom 3 319 1.2× 241 0.9× 401 2.7× 29 0.3× 78 1.1× 4 654
Charlie Saillard France 6 379 1.4× 146 0.5× 433 2.9× 82 0.9× 61 0.8× 12 754
Benoît Schmauch France 8 370 1.3× 126 0.5× 448 3.0× 82 0.9× 58 0.8× 14 763
Saba Shafi United States 10 123 0.4× 135 0.5× 126 0.9× 34 0.4× 50 0.7× 42 387

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-authorship network of co-authors of Max Schmitt

This figure shows the co-authorship network connecting the top 25 collaborators of Max Schmitt. A scholar is included among the top collaborators of Max Schmitt 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 Max Schmitt. Max Schmitt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Wessels, Frederik, Max Schmitt, Eva Krieghoff‐Henning, et al.. (2023). A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma. World Journal of Urology. 41(8). 2233–2241. 13 indexed citations
2.
Wessels, Frederik, Max Schmitt, Eva Krieghoff‐Henning, et al.. (2022). Deep learning can predict survival directly from histology in clear cell renal cell carcinoma. PLoS ONE. 17(8). e0272656–e0272656. 23 indexed citations
3.
Maron, Roman C., Achim Hekler, Eva Krieghoff‐Henning, et al.. (2021). Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study. Journal of Medical Internet Research. 23(3). e21695–e21695. 11 indexed citations
4.
Schmitt, Max, Roman C. Maron, Achim Hekler, et al.. (2021). Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study. Journal of Medical Internet Research. 23(2). e23436–e23436. 40 indexed citations
5.
Wessels, Frederik, Max Schmitt, Eva Krieghoff‐Henning, et al.. (2021). Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer. British Journal of Urology. 128(3). 352–360. 46 indexed citations
6.
Maron, Roman C., Jochen Utikal, Achim Hekler, et al.. (2020). Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study. Journal of Medical Internet Research. 22(9). e18091–e18091. 47 indexed citations
7.
Jutzi, Tanja, Eva Krieghoff‐Henning, Tim Holland‐Letz, et al.. (2020). Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective. Frontiers in Medicine. 7. 233–233. 106 indexed citations
8.
Marinov, Marin B., et al.. (2020). System Setup for Synchronized Visual-Inertial Localization and Mapping. Opus-HSO (Offenburg University of Applied Sciences). abs 1711 10250. 1–4.
9.
Kutzner, Heinz, Tanja Jutzi, Dieter Krahl, et al.. (2020). Overdiagnosis of melanoma – causes, consequences and solutions. JDDG Journal der Deutschen Dermatologischen Gesellschaft. 18(11). 1236–1243. 28 indexed citations
10.
Hekler, Achim, Jochen Utikal, Alexander Enk, et al.. (2019). Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. European Journal of Cancer. 118. 91–96. 184 indexed citations
11.
Schmitt, Max, et al.. (2019). Experimental Setup for Investigation and Evaluation of a Mapping and Localization System. 5. 81–84. 1 indexed citations
12.
Boehncke, Wolf‐­Henning, et al.. (2008). Nagellack-Allergie: Eine wichtige Differentialdiagnose bei Kontaktdermatitis. DMW - Deutsche Medizinische Wochenschrift. 122(27). 849–852. 5 indexed citations
13.
Grebenchtchikov, Nicolaï, Teresa Maguire, Rikke Riisbro, et al.. (2005). 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.. PubMed. 14(1). 235–9. 23 indexed citations
14.
Schmalfeldt, Barbara, Ingmar Claes, Oliver Hiller, et al.. (2003). Clinical Relevance of Matrix Metalloproteinase-13 Determined with a New Highly Specific and Sensitive ELISA in Ascitic Fluid of Advanced Ovarian Carcinoma Patients. Biological Chemistry. 384(8). 1247–1251. 19 indexed citations
15.
Schmitt, Max, F. Jänicke, Christoph Thomssen, et al.. (1993). Prognostic value of urokinase-type plasminogen activator (UPA) and its inhibitor PAI-1 in breast cancer. European Journal of Cancer. 29. S31–S31. 2 indexed citations
16.
Harbeck, Nadia, Nobuhiko Moniwa, Max Schmitt, et al.. (1991). [Flow cytometry DNA analysis of pure cell nuclei from formalin fixed paraffin sections in primary breast cancer: correlation with other prognostic factors].. PubMed. 31 Suppl 2. 299–302. 1 indexed citations

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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