762 citations · 993 across the 10 of their papers we have counts for
10 papers
Pre-examinations Improve Automated Metastases Detection on Cranial MRI
Katerina Deike-Hofmann, Dorottya Dancs, Daniel Paech +5
Materials and methods: First, a dual-time approach was assessed, for which the CNN was provided sequences of the MRI that initially depicted new MM (diagnosis MRI) as well as of a…
'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient
Christian Strack, Kelsey L. Pomykala, Heinz-Peter Schlemmer +2
With the rise in importance of personalized medicine, we trained personalized neural networks to detect tumor progression in longitudinal datasets. The model was evaluated on two d…
Prediction of low-keV monochromatic images from polyenergetic CT scans for improved automatic detection of pulmonary embolism
Constantin Seibold, Matthias A. Fink, Charlotte Goos +4
Detector-based spectral computed tomography is a recent dual-energy CT (DECT) technology that offers the possibility of obtaining spectral information. From this spectral data, dif…
Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs
Constantin Seibold, Jens Kleesiek, Heinz-Peter Schlemmer +1
The lack of fine-grained annotations hinders the deployment of automated diagnosis systems, which require human-interpretable justification for their decision process. In this pape…
Automated brain extraction of multi-sequence MRI using artificial neural networks
Fabian Isensee, Marianne Schell, Irada Tursunova +10
Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses. The majority of brain extraction a…
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…
Adversarial Networks for Prostate Cancer Detection
Simon Kohl, David Bonekamp, Heinz-Peter Schlemmer +5
The large number of trainable parameters of deep neural networks renders them inherently data hungry. This characteristic heavily challenges the medical imaging community and to ma…
Adversarial Networks for the Detection of Aggressive Prostate Cancer
Simon Kohl, David Bonekamp, Heinz-Peter Schlemmer +5
Semantic segmentation constitutes an integral part of medical image analyses for which breakthroughs in the field of deep learning were of high relevance. The large number of train…
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…