构建医疗影像领域OOD检测基准，提升AI系统可靠性
OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection
- 设计三类医学影像基准，覆盖14个数据集，分近/远域异常输入
- 24种后处理方法测试显示自然图像结论不适用于医疗场景
- 开源平台支持公平评估，适合医疗AI安全研究者使用
人工智能在医疗等关键领域的应用日益广泛，亟需确保系统在面对意外或异常输入时的可信性。本文提出开放医学影像分布外检测基准（OpenMIBOOD），涵盖三个不同医学领域的基准，包含14个数据集，划分为协变量偏移的分布内、近域异常和远域异常三类。我们在这些基准上评估了24种后处理方法，提供标准化参考以推动分布外检测方法的发展与公平比较。结果表明，自然图像领域的大规模基准发现无法推广至医疗应用，凸显医学领域专用基准的必要性。OpenMIBOOD通过降低模型暴露于训练分布外输入的风险，旨在支持医疗AI系统向更可靠、可信方向发展。代码仓库已公开：https://github.com/remic-othr/OpenMIBOOD。
原文摘要 · Abstract (English)
The growing reliance on Artificial Intelligence (AI) in critical domains such as healthcare demands robust mechanisms to ensure the trustworthiness of these systems, especially when faced with unexpected or anomalous inputs. This paper introduces the Open Medical Imaging Benchmarks for Out-Of-Distribution Detection (OpenMIBOOD), a comprehensive framework for evaluating out-of-distribution (OOD) detection methods specifically in medical imaging contexts. OpenMIBOOD includes three benchmarks from diverse medical domains, encompassing 14 datasets divided into covariate-shifted in-distribution, near-OOD, and far-OOD categories. We evaluate 24 post-hoc methods across these benchmarks, providing a standardized reference to advance the development and fair comparison of OOD detection methods. Results reveal that findings from broad-scale OOD benchmarks in natural image domains do not translate to medical applications, underscoring the critical need for such benchmarks in the medical field. By mitigating the risk of exposing AI models to inputs outside their training distribution, OpenMIBOOD aims to support the advancement of reliable and trustworthy AI systems in healthcare. The repository is available at https://github.com/remic-othr/OpenMIBOOD.
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