HookNet algorithm for the segmentation of histopathology breast tissue including ductal carcinoma in situ, invasive ductal carcinoma, invasive lobular carcinoma, non-malignant epithelium, fat and other breast tissue.

  1. Online examples
  2. More information
Overview of the network architecture

Mart van Rijthoven

Online examples

The examples below show the output of HookNet, overlayed on the processed WSIs. You can zoom in by scrolling, moving around can be done with click&drag.


We have made the pretrained networks available via algorithms on the Grand Challenge Platform. You can try out the following algorithms:


Segmentation algorithm for histopathology lung tissue.


Segmentation algorithm for histopathology breast tissue.


You can find the tensorflow implementation on our GitHub repository


Further questions regarding HookNet can be addressed to: Mart van Rijthoven.


Mart van Rijthoven

Mart van Rijthoven

PhD Candidate

Maschenka Balkenhol

Maschenka Balkenhol

Pathologist and Postdoctoral Researcher

Francesco Ciompi

Francesco Ciompi

Associate Professor


  • M. van Rijthoven, M. Balkenhol, K. Silina, J. van der Laak and F. Ciompi, "HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images", Medical Image Analysis, 2021;68:101890.