Christina Glasner

477 citations
4 papers · 279 indexed · 1 hit paper · h-index 3
Topics
Radiomics and Machine Learning in Medical Imaging (4 papers)AI in cancer detection (3 papers)Colorectal Cancer Screening and Detection (2 papers)
Partner nations
GermanyUnited Kingdom

In The Last Decade

Christina Glasner

4 papers receiving 274 citations

Hit Papers

Multistain deep learning for prediction of prognosis and ...202320262024202520234080120

Peers

Christina Glasner
Comparison fields: 5 of 45
  • Radiology, Nuclear Medicine and Imaging 153
  • Artificial Intelligence 129
  • Oncology 93
  • Pulmonary and Respiratory Medicine 62
  • Cancer Research 53
Replace Aurélie Fernandez with:
Aurélie Fernandez Germany
Ann-Christin Woerl Germany
Philipp Stenzel Germany
Saba Shafi United States
Si-Cong Ma China
Charles Maussion France
Manuela Vecsler United States
Oliver Lester Saldanha Germany
Kyunghyun Paeng South Korea
Samantha Bove Italy
Christina Glasner relative to Aurélie Fernandez Germany Aurélie Fernandez's profile →
Citations per field
00.5×1.5×
Aurélie Fernandez · 1×
Citations per year

Countries citing papers authored by Christina Glasner

Since Specialization
Citations

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

Fields of papers citing papers by Christina Glasner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Christina Glasner

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

All Works

4 of 4 papers shown
#WorkIndexed citations
1 2
2
Multistain deep learning for prediction of prognosis and therapy response in colorectal cancerbreakdown →
141
3 62
4 74

About Christina Glasner

Christina Glasner is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Oncology, having authored 4 papers that have together received 279 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (4 papers), AI in cancer detection (3 papers) and Colorectal Cancer Screening and Detection (2 papers). The work is most often cited by research in Health Informatics (27 citations), Radiology, Nuclear Medicine and Imaging (153 citations) and Artificial Intelligence (129 citations). Christina Glasner has collaborated with scholars based in Germany and United Kingdom. Frequent co-authors include Stefan Schulz, Sebastian Foersch, Wilfried Roth, Ann-Christin Woerl, Daniel‐Christoph Wagner, Aurélie Fernandez, Markus Eckstein, Arndt Hartmann, A. Heintz and Jakob Nikolas Kather. Their work appears in journals such as Nature Medicine, Annals of Oncology and Thyroid.

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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