98 citations · 130 across the 8 of their papers we have counts for
8 papers
Safety and accuracy follow different scaling laws in clinical large language models
Sebastian Wind, Tri-Thien Nguyen, Jeta Sopa +9
Clinical LLMs are often scaled by increasing model size, context length, retrieval complexity, or inference-time compute, with the implicit expectation that higher accuracy implies…
Slice-wise quality assessment of high b-value breast DWI via deep learning-based artifact detection
Ameya Markale, Luise Brock, Ihor Horishnyi +10
Diffusion-weighted imaging (DWI) can support lesion detection and characterization in breast magnetic resonance imaging (MRI), however especially high b-value diffusion-weighted ac…
Adapted Foundation Models for Breast MRI Triaging in Contrast-Enhanced and Non-Contrast Enhanced Protocols
Tri-Thien Nguyen, Lorenz A. Kapsner, Tobias Hepp +13
Background: Magnetic resonance imaging (MRI) has high sensitivity for breast cancer detection, but interpretation is time-consuming. Artificial intelligence may aid in pre-screenin…
Towards Super-Resolution CEST MRI for Visualization of Small Structures
Lukas Folle, Katharian Tkotz, Fasil Gadjimuradov +9
The onset of rheumatic diseases such as rheumatoid arthritis is typically subclinical, which results in challenging early detection of the disease. However, characteristic changes…
Design of a non-linear human breast diffusion coil
Feng Jia, Sebastian Littin, Philipp Amrein +8
Diffusion-weighted imaging (DWI) in the female breast is a magnetic resonance imaging (MRI) technique, which may potentially replace or effectively complement both x-ray mammograph…
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
Paul F. Jaeger, Simon A. A. Kohl, Sebastian Bickelhaupt +4
The task of localizing and categorizing objects in medical images often remains formulated as a semantic segmentation problem. This approach, however, only indirectly solves the co…
Domain Adaptation for Deviating Acquisition Protocols in CNN-based Lesion Classification on Diffusion-Weighted MR Images
Jennifer Kamphenkel, Paul F. Jaeger, Sebastian Bickelhaupt +8
End-to-end deep learning improves breast cancer classification on diffusion-weighted MR images (DWI) using a convolutional neural network (CNN) architecture. A limitation of CNN as…
Revealing Hidden Potentials of the q-Space Signal in Breast Cancer
Paul Jaeger, Sebastian Bickelhaupt, Frederik Bernd Laun +10
Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies. Diffusion…