activity
20172024
most citedDALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images

97 citations · 118 across the 18 of their papers we have counts for

collaborators

18 papers

eess.IV2024

Precision ICU Resource Planning: A Multimodal Model for Brain Surgery Outcomes

Maximilian Fischer, Florian M. Hauptmann, Robin Peretzke +4

Although advances in brain surgery techniques have led to fewer postoperative complications requiring Intensive Care Unit (ICU) monitoring, the routine transfer of patients to the…

cs.CV2024

Decoupling Semantic Similarity from Spatial Alignment for Neural Networks

Tassilo Wald, Constantin Ulrich, Gregor Köhler +6

What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions…

eess.IV20241 cited

Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution

Maximilian Fischer, Peter Neher, Tassilo Wald +9

Processing histopathological Whole Slide Images (WSI) leads to massive storage requirements for clinics worldwide. Even after lossy image compression during image acquisition, addi…

eess.IV2024

Mitigating False Predictions In Unreasonable Body Regions

Constantin Ulrich, Catherine Knobloch, Julius C. Holzschuh +7

Despite considerable strides in developing deep learning models for 3D medical image segmentation, the challenge of effectively generalizing across diverse image distributions pers…

eess.IV20245 cited

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…

eess.IV202497 cited

DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images

Michael Götz, Christian Weber, Franciszek Binczyk +7

We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for a…