1.7k citations · 2.6k across the 8 of their papers we have counts for
8 papers
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
Nuclei instance segmentation and classification in histopathology images with StarDist
Martin Weigert, Uwe Schmidt
Instance segmentation and classification of nuclei is an important task in computational pathology. We show that StarDist, a deep learning nuclei segmentation method originally dev…
Prediction error-driven memory consolidation for continual learning. On the case of adaptive greenhouse models
Guido Schillaci, Luis Miranda, Uwe Schmidt
This work presents an adaptive architecture that performs online learning and faces catastrophic forgetting issues by means of episodic memories and prediction-error driven memory…
Practical sensorless aberration estimation for 3D microscopy with deep learning
Debayan Saha, Uwe Schmidt, Qinrong Zhang +6
Estimation of optical aberrations from volumetric intensity images is a key step in sensorless adaptive optics for 3D microscopy. Recent approaches based on deep learning promise a…
An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnostics
Sarah Schmell, Falk Zakrzewski, Walter de Back +10
Histo-pathological diagnostics are an inherent part of the everyday work but are particularly laborious and associated with time-consuming manual analysis of image data. In order t…
Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy
Martin Weigert, Uwe Schmidt, Robert Haase +2
Accurate detection and segmentation of cell nuclei in volumetric (3D) fluorescence microscopy datasets is an important step in many biomedical research projects. Although many auto…