Paper accepted in Frontiers in Computer Science

Our paper on “Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images” by Niccolò Marini et al. has been published in Frontiers in Computer Science, section Digital Public Health.

This paper describes our python library called Multi_Scale_Tools. It includes four components:

  • a pre-processing component with two methods to split the WSIs in patches from arbitrary magnification levels,
  • a CNN-based scale regressor to predict the magnification in a continuous range between 5-40x,
  • two multi-scale CNN architectures to make predictions at patch-level and
  • a multi-scale CNN architecture called HookNet to segment WSIs.

The code is available on GitHub: https://github.com/sara-nl/multi-scale-tools

An example of WSI format including multiple magnification levels.
An example of tissue represented at multiple magnification level (5x, 10x, 20x, 40x).
An example of the multicenter extraction method.
Overview of a multi-scale CNN architecture.