Search Results for author:
Found 11 papers, 6 papers with code
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
Nuclei instance segmentation and classification in histopathology images with StarDist
Instance segmentation and classification of nuclei is an important task in computational pathology.
Practical sensorless aberration estimation for 3D microscopy with deep learning
Additionally, we study the predictability of individual aberrations with respect to their data requirements and find that the symmetry of the wavefront plays a crucial role.
An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnostics
It mimics the pathological assessment and relies on the detection and localization of interphase nuclei based on instance segmentation networks.
Prediction error-driven memory consolidation for continual learning. On the case of adaptive greenhouse models
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 consolidation.
Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy
Accurate detection and segmentation of cell nuclei in volumetric (3D) fluorescence microscopy datasets is an important step in many biomedical research projects.
Cell Detection with Star-convex Polygons
Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications.
Learning to Push the Limits of Efficient FFT-Based Image Deconvolution
This work addresses the task of non-blind image deconvolution.
Shrinkage Fields for Effective Image Restoration
To that end we propose shrinkage fields, a random field-based architecture that combines the image model and the optimization algorithm in a single unit.
Cascades of Regression Tree Fields for Image Restoration
We posit two reasons for this: First, the blur kernel is often only known at test time, requiring any discriminative approach to cope with considerable variability.
Discriminative Non-blind Deblurring
From this analysis, we derive a discriminative model cascade for image deblurring.
