MedMNIST-C：通过模拟真实图像损坏的综合基准与改进的分类器鲁棒性
MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions
AI总结：
本文提出了开源基准数据集MedMNIST-C，涵盖12个数据集和9种成像模态，通过模拟真实图像损坏评估算法鲁棒性，并利用领域知识进行针对性数据增强，显著提升了医学图像分类器的鲁棒性。
摘要：
基于神经网络的系统在临床实践中的整合受到域泛化和鲁棒性相关挑战的限制。计算机视觉社区建立了如ImageNet-C等基准作为衡量这些挑战进展的基本前提。医学成像社区很大程度上缺乏类似的数据集，没有涵盖多种成像模态和应用的综合性基准。为了填补这一空白，我们创建并开源了MedMNIST-C，这是一个基于MedMNIST+集合的基准数据集，涵盖12个数据集和9种成像模态。我们模拟了不同严重程度的任务和模态特定的图像损坏，以全面评估既有算法对真实世界伪影和分布偏移的鲁棒性。我们进一步提供了定量证据，表明我们易于使用的人工损坏能够实现高性能、轻量级的数据增强，从而提升模型鲁棒性。与传统的通用增强策略不同，我们的方法利用了领域知识，与广泛采用的方法相比展现出显著更高的鲁棒性。通过引入MedMNIST-C并开源允许进行针对性数据增强的相应库，我们促进了针对医学成像挑战量身定制的日益鲁棒的方法的发展。代码可在https://github.com/francescodisalvo05/medmnistc-api获取。
英文摘要：
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 .
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