PAC Bayesian Performance Guarantees for Deep (Stochastic) Networks in Medical Imaging.

ABSTRACT: Application of deep neural networks to medical imaging tasks has in some sense become commonplace. Still, a "thorn in the side" of the deep learning movement is the argument that deep networks are prone to overfitting and are thus unable to generalize well when datasets are small (as is common in medical imaging tasks). One way to bolster confidence is to provide mathematical guarantees, or bounds, on network performance after training which explicitly quantify the possibility of overfitting. In this work, we explore recent advances using the PAC-Bayesian framework to provide bounds on generalization error for large (stochastic) networks. While previous efforts focus on classification in larger natural image datasets (e.g., MNIST and CIFAR-10), we apply these techniques to both classificat

SUBMITTER: Sicilia A 

PROVIDER: S-EPMC8702021 | biostudies-literature | 2021 Sep-Oct

REPOSITORIES: biostudies-literature