REET: Robustness Evaluation and Enhancement Toolbox for Computational Pathology

TL;DR

The paper introduces REET, a comprehensive toolbox designed to evaluate and improve the robustness of deep learning models in computational pathology against various image variations and adversarial attacks.

Contribution

It presents the first domain-specific toolbox for robustness assessment and enhancement in computational pathology, including algorithms for diverse image transformations and robust training strategies.

Findings

REET effectively assesses model robustness to staining, compression, and other variations.

It enables robust training pipelines for improved model reliability.

The toolbox is publicly available for research and clinical use.

Abstract

Motivation: Digitization of pathology laboratories through digital slide scanners and advances in deep learning approaches for objective histological assessment have resulted in rapid progress in the field of computational pathology (CPath) with wide-ranging applications in medical and pharmaceutical research as well as clinical workflows. However, the estimation of robustness of CPath models to variations in input images is an open problem with a significant impact on the down-stream practical applicability, deployment and acceptability of these approaches. Furthermore, development of domain-specific strategies for enhancement of robustness of such models is of prime importance as well. Implementation and Availability: In this work, we propose the first domain-specific Robustness Evaluation and Enhancement Toolbox (REET) for computational pathology applications. It provides a suite of algorithmic strategies for enabling robustness assessment of predictive models with respect to specialized image transformations such as staining, compression, focusing, blurring, changes in spatial resolution, brightness variations, geometric changes as well as pixel-level adversarial perturbations. Furthermore, REET also enables efficient and robust training of deep learning pipelines in computational pathology. REET is implemented in Python and is available at the following URL: https://github.com/alexjfoote/reetoolbox. Contact: Fayyaz.minhas@warwick.ac.uk