Improving the repeatability of deep learning models with Monte Carlo dropout.

ABSTRACT: The integration of artificial intelligence into clinical workflows requires reliable and robust models. Repeatability is a key attribute of model robustness. Ideal repeatable models output predictions without variation during independent tests carried out under similar conditions. However, slight variations, though not ideal, may be unavoidable and acceptable in practice. During model development and evaluation, much attention is given to classification performance while model repeatability is rarely assessed, leading to the development of models that are unusable in clinical practice. In this work, we evaluate the repeatability of four model types (binary classification, multi-class classification, ordinal classification, and regression) on images that were acquired from the same patient

SUBMITTER: Lemay A 

PROVIDER: S-EPMC9674698 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature