# REET: Robustness Evaluation and Enhancement Toolbox for Computational Pathology

arXiv: https://arxiv.org/abs/2201.12311 · HTML: https://mlsanity.com/paper/2201.12311/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary](https://mlsanity.com/paper/2006.13932/) (2020)
- [Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology](https://mlsanity.com/paper/2106.08153/) (2021)

## Built on by (most cited first, 5 of 5)

- [CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting](https://mlsanity.com/paper/2303.06274/) (2023)
- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification](https://mlsanity.com/paper/2208.11052/) (2022)
- [A protocol for evaluating robustness to H&E staining variation in computational pathology models](https://mlsanity.com/paper/2603.12886/) (2026)
- [The Good, the Bad, and the Brittle: Benchmarking Robustness and Generalisation of Histopathology Foundation Models](https://mlsanity.com/paper/2607.04401/) (2026)

## Abstract (arXiv)

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