MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions

TL;DR

MedMNIST-C is a comprehensive benchmark dataset for medical imaging that simulates realistic image corruptions across multiple modalities, enabling evaluation and enhancement of model robustness through domain-informed data augmentation.

Contribution

The paper introduces MedMNIST-C, a new benchmark dataset with simulated corruptions, and demonstrates its effectiveness for improving model robustness in medical imaging.

Findings

Artificial corruptions improve robustness more than generic augmentations

MedMNIST-C covers 12 datasets and 9 imaging modalities

Domain-specific augmentations outperform traditional methods

Abstract

The integration of neural-network-based systems into clinical practice is limited by challenges related to domain generalization and robustness. The computer vision community established benchmarks such as ImageNet-C as a fundamental prerequisite to measure progress towards those challenges. Similar datasets are largely absent in the medical imaging community which lacks a comprehensive benchmark that spans across imaging modalities and applications. To address this gap, we create and open-source MedMNIST-C, a benchmark dataset based on the MedMNIST+ collection covering 12 datasets and 9 imaging modalities. We simulate task and modality-specific image corruptions of varying severity to comprehensively evaluate the robustness of established algorithms against real-world artifacts and distribution shifts. We further provide quantitative evidence that our simple-to-use artificial corruptions allow for highly performant, lightweight data augmentation to enhance model robustness. Unlike traditional, generic augmentation strategies, our approach leverages domain knowledge, exhibiting significantly higher robustness when compared to widely adopted methods. By introducing MedMNIST-C and open-sourcing the corresponding library allowing for targeted data augmentations, we contribute to the development of increasingly robust methods tailored to the challenges of medical imaging. The code is available at https://github.com/francescodisalvo05/medmnistc-api .