Hrafn Weishaupt

GlomExtractor: a versatile tool for extracting glomerular patches from whole slide kidney biopsy images

This paper introduces GlomExtractor, a tool for extracting glomerular patches from kidney biopsy images, and shows how processing strategies affect machine learning performance.

StainStyleSampler: Clustering-based sampling of whole slide image appearances

StainStyleSampler clusters and samples stain styles from whole slide images to improve machine learning robustness across different staining appearances.

Morphometry-based detection of deep learning faults in glomerular segmentation

This study uses shape analysis to detect and prioritize errors in deep learning-based glomerular segmentation, enabling more efficient correction by pathologists.

Image Analysis for Non-Neoplastic Kidney Disease: Utilizing Morphological Segmentation to Improve Quantification of Interstitial Fibrosis

This paper improves quantification of interstitial fibrosis in kidney biopsies by using morphological segmentation, showing better correlation with expert grading and reduced bias.

Unsupervised learning for labeling global glomerulosclerosis

The paper investigates the feasibility of unsupervised clustering for classifying globally sclerosed versus non-globally sclerosed glomeruli using pre-trained CNN models, achieving over 95% accuracy in most datasets.

Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

The paper infers a gene regulatory network (GRN) from perturbation experiments in cancer cells, identifying both known and novel regulatory interactions that contribute to oncogenic processes.