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20172026
most citedRetina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

98 citations · 130 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.CV2026

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…

cs.CV2025★ 1 cited

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…

cs.CV2018★ 98 cited

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…

cs.CV2018

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…

cs.CV2017★ 11 cited

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…