在模型优越性的统计声明中考虑欠规范问题
Accounting for Underspecification in Statistical Claims of Model Superiority
AI总结：
本文提出一种统计框架，将训练随机性引入的欠规范作为额外方差分量，用于建模模型优越性声明中的假阳性，并证明微小种子变异性即可显著增加支持优越性所需的证据，强调医学成像验证需显式建模训练方差。
摘要：
机器学习方法日益应用于医学成像，但许多已发表的改进缺乏统计稳健性：近期研究强调，微弱但显著的性能提升极有可能是假阳性。然而，这些分析未将欠规范问题纳入考量——即因随机初始化或训练动态，在相同数据上达到相似验证分数的模型可能对未见数据表现迥异。本文扩展了近期统计框架，对假阳性声明进行建模，引入欠规范作为额外方差分量。我们的模拟表明，即使微小的种子变异性（~1%）也会显著增加支持优越性声明所需的证据。我们的发现强调，在验证医学成像系统时，需显式建模训练方差以进行测试。
英文摘要：
Machine learning methods are increasingly applied in medical imaging, yet many reported improvements lack statistical robustness: recent works have highlighted that small but significant performance gains are highly likely to be false positives. However, these analyses do not take \emph{underspecification} into account -- the fact that models achieving similar validation scores may behave differently on unseen data due to random initialization or training dynamics. Here, we extend a recent statistical framework modeling false outperformance claims to include underspecification as an additional variance component. Our simulations demonstrate that even modest seed variability ($\sim1\%$) substantially increases the evidence required to support superiority claims. Our findings underscore the need for explicit modeling of training variance when validating medical imaging systems.
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