Maximilian Knoll

1.1k citations
39 papers · 516 indexed · h-index 11
Topics
Glioma Diagnosis and Treatment (12 papers)Radiomics and Machine Learning in Medical Imaging (11 papers)Medical Imaging Techniques and Applications (6 papers)

In The Last Decade

Maximilian Knoll

34 papers receiving 510 citations

Peers

Maximilian Knoll
Comparison fields: 5 of 102
  • Pulmonary and Respiratory Medicine 123
  • Molecular Biology 121
  • Oncology 107
  • Radiology, Nuclear Medicine and Imaging 99
  • Genetics 92
Replace Yeon-Joo Kim with:
Yeon-Joo Kim South Korea
Stanislav Lazarev United States
Elham Rahimy United States
Dustin Boothe United States
Michael Jonathan Kucharczyk Canada
Orla McArdle Ireland
Sara Kinhult Sweden
Jessica A. Smith United States
Katherine Hansen United States
David L. Kok Australia
Maximilian Knoll relative to Yeon-Joo Kim South Korea Yeon-Joo Kim's profile →
Citations per field
00.5×5.9×
Yeon-Joo Kim · 1×
Citations per year

Countries citing papers authored by Maximilian Knoll

Since Specialization
Citations

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

Fields of papers citing papers by Maximilian Knoll

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maximilian Knoll

This figure shows the co-authorship network connecting the top 25 collaborators of Maximilian Knoll. A scholar is included among the top collaborators of Maximilian Knoll 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 Maximilian Knoll. Maximilian Knoll 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 1
2 0
3 1
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5 2
6 0
7 0
8 0
9 10
10 24
11 1
12 7
13 5
14 3
15 6
16 176
17 15
18 60
19 31
20 21

About Maximilian Knoll

Maximilian Knoll is a scholar working on Genetics, Radiology, Nuclear Medicine and Imaging and Small Animals, having authored 39 papers that have together received 516 indexed citations. Recurring topics across this work include Glioma Diagnosis and Treatment (12 papers), Radiomics and Machine Learning in Medical Imaging (11 papers) and Medical Imaging Techniques and Applications (6 papers). The work is most often cited by research in Genetics (92 citations), Radiation (45 citations) and Cancer Research (75 citations). Maximilian Knoll has collaborated with scholars based in Germany, United States and Egypt. Frequent co-authors include Amir Abdollahi, Jürgen Debus, Jennifer Furkel, André Karch, Christian Stock, Wolfgang Wick, Uwe Haberkorn, Christel Herold‐Mende, Zili Tang and Ivana Đokić. Their work appears in journals such as Journal of Clinical Oncology, Blood and 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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