Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images
Gustav Müller‐Franzes(Universitätsklinikum Aachen), Daniel Truhn(Universitätsklinikum Aachen), Firas Khader(RWTH Aachen University), Christiane Kühl(Universitätsklinikum Aachen), Luisa Huck(RWTH Aachen University), Volkmar Schulz(RWTH Aachen University), Jakob Nikolas Kather(Heidelberg University), Soroosh Tayebi Arasteh(Friedrich-Alexander-Universität Erlangen-Nürnberg), Sven Nebelung(Universitätsklinikum Aachen), Ebba Dethlefsen(RWTH Aachen University), Teresa Nolte(Universitätsklinikum Aachen), Tianyu Han(RWTH Aachen University)
Cited by 67
Related Papers
Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
|Nature Medicine|2019|1.4k
Mammography, Breast Ultrasound, and Magnetic Resonance Imaging for Surveillance of Women at High Familial Risk for Breast Cancer
|Journal of Clinical Oncology|2005|1.1k
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
|PLoS Medicine|2019|1k
The future landscape of large language models in medicine
|Communications Medicine|2023|937
Deep learning in cancer pathology: a new generation of clinical biomarkers
|British Journal of Cancer|2020|649