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Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

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REET: robustness evaluation and enhancement toolbox for computational pathology.

Alex Foote¹, Amina Asif¹, Nasir Rajpoot¹

¹Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.

Bioinformatics (Oxford, England)

|May 9, 2022

Summary

This study introduces a new toolbox for computational pathology (CPath) to assess and improve the robustness of deep learning models against image variations. This enhances the reliability of CPath in clinical applications.

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Area of Science:

Computational pathology
Digital pathology
Deep learning in histology

Background:

Digital slide scanners and deep learning advance computational pathology (CPath).
Model robustness to image variations is crucial for CPath deployment but remains an open problem.
Domain-specific strategies are needed to enhance CPath model robustness.

Purpose of the Study:

To introduce the first domain-specific Robustness Evaluation and Enhancement Toolbox (REET) for CPath.
To provide strategies for assessing CPath model robustness against various image transformations.
To enable efficient and robust training of deep learning models in CPath.

Main Methods:

Development of the Robustness Evaluation and Enhancement Toolbox (REET).
Inclusion of algorithmic strategies for robustness assessment against image transformations (staining, compression, focus, etc.).
Implementation of methods for pixel-level adversarial perturbations and robust deep learning training.

Main Results:

The REET toolbox offers comprehensive robustness assessment for CPath models.
REET supports evaluation against diverse image variations relevant to digital pathology.
The toolbox facilitates robust training of deep learning pipelines for CPath applications.

Conclusions:

REET addresses the critical need for robust CPath models.
The toolbox enhances the practical applicability and reliability of computational pathology.
REET is available as an open-source Python implementation.