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 100.363 Papers
A survey on deep learning in medical image analysis
2017 · 13.483 Zit.
Dermatologist-level classification of skin cancer with deep neural networks
2017 · 13.116 Zit.
A survey on Image Data Augmentation for Deep Learning
2019 · 11.718 Zit.
QuPath: Open source software for digital pathology image analysis
2017 · 8.074 Zit.
Radiomics: Images Are More than Pictures, They Are Data
2015 · 7.969 Zit.
Unified segmentation
2005 · 7.412 Zit.
A method of comparing the areas under receiver operating characteristic curves derived from the same cases.
1983 · 7.061 Zit.
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
2014 · 6.219 Zit.
ImageJ2: ImageJ for the next generation of scientific image data
2017 · 6.098 Zit.
Radiomics: Extracting more information from medical images using advanced feature analysis
2012 · 5.727 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.
Integrated Biomedical Signal and Image Processing Techniques for Enhanced Disease Diagnosis and Clinical Decision Support
2026-03-10 · 0 Zit.
Personalized Medicine and ProcessOptimization: Analysis and Implementation ofIntelligent Tools to Support the Clinical Process
2026-03-10 · 0 Zit.
Application and Practice of a 3D Model–Driven Clinically Oriented, Three-Stage Progressive Teaching Program in Histology and Embryology
2026-03-09 · 0 Zit.
A Cautionary Note in the Era of Computer Aided Detection of Polyps at Colonoscopy and Need for Human Centered Design
2026-03-09 · 0 Zit.
Automatic calculation of pelvis morphology from CT images
2026-03-09 · 0 Zit.
Pathology Public Datasets for Artificial Intelligence: A Systematic Review
2026-03-09 · 0 Zit.
Desarrollo de nuevos biomarcadores de imagen para la detección de cáncer de mama
2026-03-09 · 0 Zit.
Top 8 Autoren
von 133.584 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
Suez University
Chris Taylor
University of Leeds
Stephen M. Hewitt
National Institutes of Health
U. Rajendra Acharya
University of Southern Queensland
Top 8 Institutionen
von 901 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