Overview of four different corruptions applied (from top to bottom) to PathMNIST, ChestMNIST, DermaMNIST, and RetinaMNIST.

Overview of four different corruptions applied (from top to bottom) to PathMNIST, ChestMNIST, DermaMNIST, and RetinaMNIST.

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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 imagin...

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... Hendrycks et al. [10] and subsequent medical imaging benchmarks [18,33,13], we evaluate model robustness across 5 severity levels. Therefore, we carefully reviewed, identified, and then described how those severity levels can be mapped to a broad set of medical imaging datasets, as illustrated in Figure 1. PathMNIST and BloodMNIST Pathology and blood cell microscopy share similar imaging protocols, leading to common artifacts [33,32,13]. ...