19 citations · 39 across the 5 of their papers we have counts for
11 papers
Seeded iterative clustering for histology region identification
Eduard Chelebian, Francesco Ciompi, Carolina Wählby
Annotations are necessary to develop computer vision algorithms for histopathology, but dense annotations at a high resolution are often time-consuming to make. Deep learning model…
Quantifying the Scanner-Induced Domain Gap in Mitosis Detection
Marc Aubreville, Christof Bertram, Mitko Veta +6
Automated detection of mitotic figures in histopathology images has seen vast improvements, thanks to modern deep learning-based pipelines. Application of these methods, however, i…
Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer
Caner Mercan, Maschenka Balkenhol, Roberto Salgado +13
Nuclear pleomorphism, defined herein as the extent of abnormalities in the overall appearance of tumor nuclei, is one of the components of the three-tiered breast cancer grading. G…
HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images
Mart van Rijthoven, Maschenka Balkenhol, Karina Siliņa +2
We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional n…
Extending Unsupervised Neural Image Compression With Supervised Multitask Learning
David Tellez, Diederik Hoppener, Cornelis Verhoef +5
We focus on the problem of training convolutional neural networks on gigapixel histopathology images to predict image-level targets. For this purpose, we extend Neural Image Compre…
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…