632 citations · 1.5k across the 5 of their papers we have counts for
5 papers
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…
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
David Tellez, Geert Litjens, Peter Bandi +4
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift b…
Neural Image Compression for Gigapixel Histopathology Image Analysis
David Tellez, Geert Litjens, Jeroen van der Laak +1
We propose Neural Image Compression (NIC), a two-step method to build convolutional neural networks for gigapixel image analysis solely using weak image-level labels. First, gigapi…
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
David Tellez, Maschenka Balkenhol, Irene Otte-Holler +10
Manual counting of mitotic tumor cells in tissue sections constitutes one of the strongest prognostic markers for breast cancer. This procedure, however, is time-consuming and erro…
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
Mitko Veta, Yujing J. Heng, Nikolas Stathonikos +30
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is highly subjective an…