Search Results for author: Maschenka Balkenhol

Found 6 papers, 1 papers with code

Domain adaptation strategies for cancer-independent detection of lymph node metastases

no code implementations13 Jul 2022 Péter Bándi, Maschenka Balkenhol, Marcory van Dijk, Bram van Ginneken, Jeroen van der Laak, Geert Litjens

Furthermore, we show the effectiveness of repeated adaptation of networks from one cancer type to another to obtain multi-task metastasis detection networks.

Cancer Metastasis Detection Domain Adaptation

HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

1 code implementation22 Jun 2020 Mart van Rijthoven, Maschenka Balkenhol, Karina Siliņa, Jeroen van der Laak, Francesco Ciompi

We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks.

Image Segmentation Segmentation +3

Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

no code implementations17 Aug 2018 David Tellez, Maschenka Balkenhol, Irene Otte-Holler, Rob van de Loo, Rob Vogels, Peter Bult, Carla Wauters, Willem Vreuls, Suzanne Mol, Nico Karssemeijer, Geert Litjens, Jeroen van der Laak, Francesco Ciompi

Application of CNNs to hematoxylin and eosin (H&E) stained histological tissue sections is hampered by: (1) noisy and expensive reference standards established by pathologists, (2) lack of generalization due to staining variation across laboratories, and (3) high computational requirements needed to process gigapixel whole-slide images (WSIs).

Data Augmentation Knowledge Distillation +2

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