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SliDL: A toolbox for processing whole-slide images in deep learning.

Adam G Berman1, William R Orchard1, Marcel Gehrung1

  • 1Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom.

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Summary

SliDL is a new Python library that simplifies the analysis of whole-slide images (WSIs) for deep learning applications. This tool facilitates essential pre- and post-processing tasks, making WSI analysis more accessible for disease diagnosis.

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Area of Science:

  • Digital pathology
  • Computational biology
  • Medical image analysis

Background:

  • Pathologist review of stained tissue slides is crucial for disease detection and diagnosis.
  • Deep learning (DL) shows promise in analyzing whole-slide images (WSIs) to aid pathologists.
  • WSIs present unique challenges including artifacts, annotations, and performance evaluation.

Purpose of the Study:

  • Introduce SliDL, a Python library for WSI pre- and post-processing.
  • Simplify WSI data handling for deep learning workflows.
  • Increase accessibility of DL for WSI analysis in pathology.

Main Methods:

  • Developed SliDL, a Python library for WSI data handling.
  • Implemented functionalities for annotation, tile extraction, tissue detection, and model evaluation.
  • Designed SliDL for seamless integration with PyTorch deep learning library.

Main Results:

  • SliDL streamlines essential WSI processing tasks with simple code.
  • The library bridges standard image analysis and specialized WSI analysis.
  • Provided code snippets to guide users in applying SliDL functionalities.

Conclusions:

  • SliDL enhances the accessibility and efficiency of deep learning for WSI analysis.
  • The library supports the development and application of DL methods in digital pathology.
  • Facilitates easier integration of WSI analysis into clinical diagnostic workflows.