How to learn with intentional mistakes: NoisyEnsembles to overcome poor tissue quality for deep learning in computational pathology.

ABSTRACT: There is a lot of recent interest in the field of computational pathology, as many algorithms are introduced to detect, for example, cancer lesions or molecular features. However, there is a large gap between artificial intelligence (AI) technology and practice, since only a small fraction of the applications is used in routine diagnostics. The main problems are the transferability of convolutional neural network (CNN) models to data from other sources and the identification of uncertain predictions. The role of tissue quality itself is also largely unknown. Here, we demonstrated that samples of the TCGA ovarian cancer (TCGA-OV) dataset from different tissue sources have different quality characteristics and that CNN performance is linked to this property. CNNs performed best on high-quali

SUBMITTER: Mayer RS 

PROVIDER: S-EPMC9464871 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature