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:

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.