Random effects during training : Implications for deep learning-based medical image segmentation
Background: A single learning algorithm can produce deep learning-based image segmentation models that vary in performance purely due to random effects during training. This study assessed the effect of these random performance fluctuations on the reliability of standard methods of comparing segmentation models. Methods: The influence of random effects during training was assessed by running a sin
