19 citations · 21 across the 4 of their papers we have counts for
4 papers
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…
Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images
Babak Ehteshami Bejnordi, Jimmy Linz, Ben Glass +6
Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, m…