3 citations · 4 across the 6 of their papers we have counts for
6 papers
Vision-Language Models as Zero-Annotation Oracles in Histopathology
Vishal Jain, Giorgio Buzzanca, Sarah Cechnicka +6
Foreground segmentation is the critical first step of every computational pathology pipeline, yet existing methods rely on hand-tuned heuristics or supervised models that overfit t…
PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning in Histopathology
Siemen Brussee, Pieter A. Valkema, Jurre A. J. Weijer +3
We introduce PathBench-MIL, an open-source AutoML and benchmarking framework for multiple instance learning (MIL) in histopathology. The system automates end-to-end MIL pipeline co…
Graph Neural Networks in Histopathology: Emerging Trends and Future Directions
Siemen Brussee, Giorgio Buzzanca, Anne M. R. Schrader +1
Histopathological analysis of Whole Slide Images (WSIs) has seen a surge in the utilization of deep learning methods, particularly Convolutional Neural Networks (CNNs). However, CN…
Segmentation of diagnostic tissue compartments on whole slide images with renal thrombotic microangiopathies (TMAs)
Huy Q. Vo, Pietro A. Cicalese, Surya Seshan +18
The thrombotic microangiopathies (TMAs) manifest in renal biopsy histology with a broad spectrum of acute and chronic findings. Precise diagnostic criteria for a renal biopsy diagn…
Advances in Kidney Biopsy Lesion Assessment through Dense Instance Segmentation
Zhan Xiong, Junling He, Pieter Valkema +4
Renal biopsies are the gold standard for the diagnosis of kidney diseases. Lesion scores made by renal pathologists are semi-quantitative and exhibit high inter-observer variabilit…
U-Net-and-a-half: Convolutional network for biomedical image segmentation using multiple expert-driven annotations
Yichi Zhang, Jesper Kers, Clarissa A. Cassol +8
Development of deep learning systems for biomedical segmentation often requires access to expert-driven, manually annotated datasets. If more than a single expert is involved in th…