Amelie Echle

2.4k total citations · 1 hit paper
10 papers, 618 citations indexed

About

Amelie Echle is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Oncology. According to data from OpenAlex, Amelie Echle has authored 10 papers receiving a total of 618 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Artificial Intelligence and 4 papers in Oncology. Recurrent topics in Amelie Echle's work include Radiomics and Machine Learning in Medical Imaging (8 papers), AI in cancer detection (7 papers) and Cancer Genomics and Diagnostics (3 papers). Amelie Echle is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (8 papers), AI in cancer detection (7 papers) and Cancer Genomics and Diagnostics (3 papers). Amelie Echle collaborates with scholars based in Germany, Netherlands and United Kingdom. Amelie Echle's 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 Nicholas P. West and has published in prestigious journals such as SHILAP Revista de lepidopterología, British Journal of Cancer and The Journal of Pathology.

In The Last Decade

Amelie Echle

10 papers receiving 609 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep learning in cancer pathology: a new generation of clinical biomarkers

2020 362 citations Amelie Echle, Niklas Rindtorff et al. British Journal of Cancer profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Amelie Echle Germany	9	362	350	198	115	84	10	618
Charlie Saillard France	6	379 1.0×	433 1.2×	146 0.7×	145 1.3×	116 1.4×	12	754
Benoît Schmauch France	8	370 1.0×	448 1.3×	126 0.6×	134 1.2×	111 1.3×	14	763
Ole-Johan Skrede United Kingdom	3	319 0.9×	401 1.1×	241 1.2×	110 1.0×	67 0.8×	4	654
Meriem Sefta France	5	347 1.0×	305 0.9×	157 0.8×	155 1.3×	143 1.7×	6	688
Pooya Mobadersany United States	5	432 1.2×	420 1.2×	105 0.5×	125 1.1×	114 1.4×	9	716
Pierre Courtiol France	4	436 1.2×	418 1.2×	164 0.8×	182 1.6×	135 1.6×	7	815
Mane Williams United States	5	431 1.2×	338 1.0×	107 0.5×	114 1.0×	127 1.5×	6	707
Richard Colling United Kingdom	18	318 0.9×	263 0.8×	252 1.3×	122 1.1×	124 1.5×	47	769
Jeremias Krause Germany	3	466 1.3×	489 1.4×	305 1.5×	198 1.7×	109 1.3×	8	868
Xiangxue Wang United States	13	339 0.9×	444 1.3×	241 1.2×	117 1.0×	99 1.2×	31	759

Countries citing papers authored by Amelie Echle

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Amelie Echle

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

All Works

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10 of 10 papers shown

1.

Echle, Amelie, et al.. (2022). The future of artificial intelligence in digital pathology – results of a survey across stakeholder groups. Histopathology. 80(7). 1121–1127. 19 indexed citations

2.

Alwers, Elizabeth, Jakob Nikolas Kather, Matthias Kloor, et al.. (2022). Validation of the prognostic value of CD3 and CD8 cell densities analogous to the Immunoscore® by stage and location of colorectal cancer: an independent patient cohort study. The Journal of Pathology Clinical Research. 9(2). 129–136. 11 indexed citations

3.

Loeffler, Chiara Maria Lavinia, Nadine T. Gaisa, Hannah Sophie Muti, et al.. (2022). Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types. Frontiers in Genetics. 12. 806386–806386. 17 indexed citations

4.

Krause, Jeremias, Heike I. Grabsch, Matthias Kloor, et al.. (2021). Deep learning detects genetic alterations in cancer histology generated by adversarial networks. The Journal of Pathology. 254(1). 70–79. 51 indexed citations

5.

Echle, Amelie, Narmin Ghaffari Laleh, Nicholas P. West, et al.. (2021). Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review. SHILAP Revista de lepidopterología. 3-4. 100008–100008. 33 indexed citations

6.

Laleh, Narmin Ghaffari, Amelie Echle, Daniel Truhn, et al.. (2021). Weakly supervised annotation‐free cancer detection and prediction of genotype in routine histopathology. The Journal of Pathology. 256(1). 50–60. 54 indexed citations

7.

Echle, Amelie, Narmin Ghaffari Laleh, Marie Louise Malmstrøm, et al.. (2021). Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer. The Journal of Pathology. 256(3). 269–281. 53 indexed citations

8.

Laleh, Narmin Ghaffari, et al.. (2021). Deep Learning for interpretable end-to-end survival (E-ESurv) prediction in gastrointestinal cancer histopathology. 81–93. 2 indexed citations

9.

Echle, Amelie, Niklas Rindtorff, Titus J. Brinker, et al.. (2020). Deep learning in cancer pathology: a new generation of clinical biomarkers. British Journal of Cancer. 124(4). 686–696. 362 indexed citations breakdown →

10.

Muti, Hannah Sophie, Chiara Maria Lavinia Loeffler, Amelie Echle, et al.. (2020). The Aachen Protocol for Deep Learning Histopathology: A hands-on guide for data preprocessing. Zenodo (CERN European Organization for Nuclear Research). 16 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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