activity
20182022
most citedRobust, fast and accurate: a 3-step method for automatic histological image registration

12 citations · 15 across the 3 of their papers we have counts for

collaborators

5 papers

q-bio.QM20223 cited

Deep Learning based Prediction of MSI using MMR Markers in Colorectal Cancer

Ruqayya Awan, Mohammed Nimir, Shan E Ahmed Raza +5

The accurate diagnosis and molecular profiling of colorectal cancers are critical for planning the best treatment options for patients. Microsatellite instability (MSI) or mismatch…

eess.IV2022

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…

eess.IV2020

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…

cs.CV201912 cited

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…

cs.CV2018

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…