2025/11/04 by Sanchez, Thomas, Gordaliza, Pedro M., Cuadra, Meritxell Bach
Computer Science · Mathematics · #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Statistical Methods in Clinical Trials
paper · doi:10.48550/arxiv.2511.02453
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 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 (∼1%) 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.