REET: Robustness Evaluation and Enhancement Toolbox for Computational Pathology
This addresses the need for reliable computational pathology models in medical and pharmaceutical research, though it is incremental as it builds on existing robustness concepts in a specific domain.
The authors tackled the problem of evaluating and enhancing the robustness of computational pathology models to various image variations, resulting in the development of REET, a domain-specific toolbox that provides algorithmic strategies for robustness assessment and training.
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