Amelie Echle
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
Papers in
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- AI in cancer detection 7
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- Radiomics and Machine Learning in Medical Imaging 7
- Co-authors
- Jakob Nikolas Kather (10 shared papers)Titus J. Brinker (5 shared papers)Alexander T. Pearson (3 shared papers)Tom Luedde (2 shared papers)Niklas Rindtorff (1 shared paper)Heike I. Grabsch (6 shared papers)Philip Quirke (4 shared papers)Narmin Ghaffari Laleh (5 shared papers)
- Journals
- The Journal of Pathology (3 papers)Frontiers in Genetics (1 paper)Histopathology (1 paper)British Journal of Cancer (1 paper)The Journal of Pathology Clinical Research (1 paper)
- Partner nations
- GermanyNetherlandsUnited Kingdom
In The Last Decade
Amelie Echle
10 papers receiving 674 citations
Amelie Echle's Hit Papers
Peers
Comparison fields: 5 of 65
- Health Informatics 63
- Radiology, Nuclear Medicine and Imaging 281
- Biophysics 70
- Artificial Intelligence 354
- Cancer Research 97
Countries citing papers authored by Amelie Echle
This map shows the geographic impact of Amelie Echle'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 Amelie Echle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amelie Echle more than expected).
Fields of papers citing papers by Amelie Echle
This network shows the impact of papers produced by Amelie Echle. 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 Amelie Echle. The network helps show where Amelie Echle may publish in the future.
Co-authors
The 25 scholars most cited alongside Amelie Echle, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Deep learning in cancer pathology: a new generation of clinical biomarkers Hit paper breakdown → | 2020 | 406 |
| 2 | 2021 | 58 | |
| 3 | 2021 | 56 | |
| 4 | 2021 | 56 | |
| 5 | 2021 | 35 | |
| 6 | 2022 | 22 | |
| 7 | 2022 | 18 | |
| 8 | 2020 | 17 | |
| 9 | 2022 | 13 | |
| 10 | Deep Learning for interpretable end-to-end survival (E-ESurv) prediction in gastrointestinal cancer histopathology | 2021 | 2 |
About Amelie Echle
Amelie Echle is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Oncology, Cancer Research and Pathology and Forensic Medicine, having authored 10 papers that have together received 683 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (7 papers), AI in cancer detection (7 papers), Cancer Genomics and Diagnostics (3 papers), Genetic factors in colorectal cancer (2 papers), Colorectal Cancer Screening and Detection (2 papers), Inflammatory Biomarkers in Disease Prognosis (1 paper), Artificial Intelligence in Healthcare and Education (1 paper) and Gene expression and cancer classification (1 paper). The work is most often cited by research in Health Informatics (63 citations), Radiology, Nuclear Medicine and Imaging (281 citations), Biophysics (70 citations), Artificial Intelligence (354 citations) and Cancer Research (97 citations). Amelie Echle has collaborated with scholars based in Germany, Netherlands and United Kingdom. Frequent co-authors include Jakob Nikolas Kather, Titus J. Brinker, Alexander T. Pearson, Tom Luedde, Niklas Rindtorff, Heike I. Grabsch, Philip Quirke, Narmin Ghaffari Laleh, Christian Trautwein and Lara R. Heij. Their work appears in journals such as The Journal of Pathology, Frontiers in Genetics, Histopathology, British Journal of Cancer and The Journal of Pathology Clinical Research.
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.