154 citations · 184 across the 5 of their papers we have counts for
6 papers
CoMeT: A foundation model for medical image analysis through federated, multidimensional context integration
J. Raphael Schäfer, Kai Geissler, Till Nicke +27
Medical foundation models improve generalization when training AI models with limited labeled data, but remain confined to a single specialty, such as pathology or radiology, and t…
Deep Feature based Cross-slide Registration
Ruqayya Awan, Shan E Ahmed Raza, Johannes Lotz +2
Cross-slide image analysis provides additional information by analysing the expression of different biomarkers as compared to a single slide analysis. These biomarker stained slide…
Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathology
Johannes Lotz, Nick Weiss, Jeroen van der Laak +1
Purpose: In digital histopathology, virtual multi-staining is important for diagnosis and biomarker research. Additionally, it provides accurate ground-truth for various deep-learn…
Virtual staining for mitosis detection in Breast Histopathology
Caner Mercan, Germonda Reijnen-Mooij, David Tellez Martin +4
We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa. We us…
Robust, fast and accurate: a 3-step method for automatic histological image registration
Johannes Lotz, Nick Weiss, Stefan Heldmann
We present a 3-step registration pipeline for differently stained histological serial sections that consists of 1) a robust pre-alignment, 2) a parametric registration computed on…
Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
Wouter Bulten, Péter Bándi, Jeffrey Hoven +7
Prostate cancer (PCa) is graded by pathologists by examining the architectural pattern of cancerous epithelial tissue on hematoxylin and eosin (H&E) stained slides. Given the impor…