Staining Invariant Features for Improving Generalization of Deep Convolutional Neural Networks in Computational Pathology.

ABSTRACT: One of the main obstacles for the implementation of deep convolutional neural networks (DCNNs) in the clinical pathology workflow is their low capability to overcome variability in slide preparation and scanner configuration, that leads to changes in tissue appearance. Some of these variations may not be not included in the training data, which means that the models have a risk to not generalize well. Addressing such variations and evaluating them in reproducible scenarios allows understanding of when the models generalize better, which is crucial for performance improvements and better DCNN models. Staining normalization techniques (often based on color deconvolution and deep learning) and color augmentation approaches have shown improvements in the generalization of the classification ta

SUBMITTER: Otalora S 

PROVIDER: S-EPMC6716536 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature