Eva Krieghoff‐Henning

Author

Also Known As

E Krieghoff-Henning, E. Krieghoff-Henning, Eva Isabelle Krieghoff-Henning, Eva I. Krieghoff-Henning, Eva I. Krieghoff‐Henning, Eva Krieghoff-Henning, Eva Krieghoff‐Henning, Krieghoff-Henning, Eva, Krieghoff-Henning, Eva I.

Publications

2.3K

Citations

H-Index

Concepts

All Affiliations

Heidelberg University German Cancer Research Center National Center for Tumor Diseases University Hospital Heidelberg Berlin Institute of Health at Charité - Universitätsmedizin Berlin

About

Eva Krieghoff‐Henning is an author at Heidelberg University specializing in medicine, pathology, and engineering.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Medicine	5	5	293
2	Engineering	3	3	214
3	Cell Biology	2	2	79
4	Data Science	1	1	58
5	Nephrology	1	1	36

Eva Krieghoff‐Henning

Publications

1.6K

Citations

H-Index

Page 1

	Year	Citations
Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts Sarah Haggenmüller, Roman C. Maron, Achim Hekler, European Journal of Cancer	2021	233
Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective Tanja Jutzi, Eva Krieghoff‐Henning, Tim Holland‐Letz, Frontiers in Medicine	2020	177
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma Tirtha Chanda, Katja Hauser, Sarah Hobelsberger, Nature Communications Artificial IntelligenceDermoscopic ImageTrustworthy Artificial IntelligenceEngineeringTrustworthy Ai	2024	120
Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification Julia Höhn, Eva Krieghoff‐Henning, Tanja Jutzi, European Journal of Cancer	2021	104
Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study Roman C. Maron, Jochen Utikal, Achim Hekler, Journal of Medical Internet Research	2020	84
HIPK2 restricts SIRT1 activity upon severe DNA damage by a phosphorylation-controlled mechanism Elisa Conrad, Tilman Polonio-Vallon, Michael T. Meister, Cell Death and Differentiation	2015	72
Deep learning can predict lymph node status directly from histology in colorectal cancer Lennard Kiehl, Sara Kuntz, Julia Höhn, European Journal of Cancer	2021	69
Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer Frederik Wessels, Max Schmitt, Eva Krieghoff‐Henning, British Journal of Urology	2021	64
Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours Titus J. Brinker, Lennard Kiehl, Max Schmitt, European Journal of Cancer	2021	63
A benchmark for neural network robustness in skin cancer classification Roman C. Maron, Justin Gabriel Schlager, Sarah Haggenmüller, European Journal of Cancer	2021	62

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