KI in der Krebserkennung
Einsatz von Deep Learning und KI-Methoden zur frühzeitigen Erkennung und Klassifikation von Tumoren.
Krebs frühzeitig zu erkennen kann Leben retten – und genau hier setzt KI an. Deep-Learning-Modelle erreichen inzwischen bei bestimmten Tumorarten eine Erkennungsgenauigkeit, die mit der erfahrener Pathologen vergleichbar ist. Die Forschung umfasst Hautkrebs-Screening, Brustkrebs-Mammographie, Lungennoduli-Erkennung und vieles mehr. Hier finden Sie die einflussreichsten und neuesten Studien zu diesem Thema.
Top 10 – Meistzitierte Papers
Top 2026von 100.857 Papers
A survey on deep learning in medical image analysis
2017 · 13.571 Zit.
Dermatologist-level classification of skin cancer with deep neural networks
2017 · 13.189 Zit.
A survey on Image Data Augmentation for Deep Learning
2019 · 11.798 Zit.
QuPath: Open source software for digital pathology image analysis
2017 · 8.183 Zit.
Radiomics: Images Are More than Pictures, They Are Data
2015 · 8.015 Zit.
Unified segmentation
2005 · 7.428 Zit.
A method of comparing the areas under receiver operating characteristic curves derived from the same cases.
1983 · 7.063 Zit.
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
2014 · 6.244 Zit.
ImageJ2: ImageJ for the next generation of scientific image data
2017 · 6.124 Zit.
Radiomics: Extracting more information from medical images using advanced feature analysis
2012 · 5.756 Zit.
Top 10 – Neueste Papers
zuletzt veröffentlicht
Gene Selection for Breast Cancer Classification Using T-Test Filtering and Wrapper-Based Optimization
2026-12-31 · 0 Zit.
Gene Selection for Breast Cancer Classification Using T-Test Filtering and Wrapper-Based Optimization
2026-12-31 · 0 Zit.
Iterative confidence-based pseudo-labeling for semi-supervised lung cancer segmentation under annotation scarcity
2026-04-08 · 0 Zit.
A Proximal Approach for Stain Separation and Normalization of Whole-Slide Histopathological Images
2026-04-08 · 0 Zit.
Study of radiomics and artificial intelligence applications in multimodal breast imaging: from conventional to advanced imaging
2026-04-02 · 0 Zit.
Digital Pathology Applications in Rare and Underrepresented Thoracic Malignancies
2026-04-02 · 0 Zit.
Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.
2026-03-25 · 0 Zit.
ROI-guided relational YOLO–SegNet transformer for lightweight bone tumor segmentation and classification from X-ray images
2026-03-23 · 0 Zit.
Open-World Medical Image Analysis: Towards Robust and Generalizable Diagnostic Intelligence
2026-03-23 · 0 Zit.
Digital pathology platforms with integrated AI algorithms: a structured landscape analysis and recommendations for clinical implementation
2026-03-23 · 0 Zit.
Top 8 Autoren
von 136.450 Autoren insgesamt
P. J. van Diest
Utrecht University
Yudong Zhang
First Affiliated Hospital of Guangzhou Medical University
Henning Müller
Central University of Technology
Robert Klopfleisch
Robert Koch Institute
Aboul Ella Hassanien
Cairo University
Chris Taylor
Boston Children's Hospital
Stephen M. Hewitt
U. Rajendra Acharya
University of Southern Queensland
Top 8 Institutionen
von 908 Institutionen insgesamt
Vellore Institute of Technology University
IN
Saveetha University
IN
Manipal Academy of Higher Education
IN
SRM Institute of Science and Technology
IN
Amrita Vishwa Vidyapeetham
IN
Lovely Professional University
IN
Prince Sattam Bin Abdulaziz University
SA
Princess Nourah bint Abdulrahman University
SA