Ensemble-based deep learning improves detection of invasive breast cancer in routine histopathology images.

ABSTRACT: Accurate detection of invasive breast cancer (IC) can provide decision support to pathologists as well as improve downstream computational analyses, where detection of IC is a first step. Tissue containing IC is characterized by the presence of specific morphological features, which can be learned by convolutional neural networks (CNN). Here, we compare the use of a single CNN model versus an ensemble of several base models with the same CNN architecture, and we evaluate prediction performance as well as variability across ensemble based model predictions. Two in-house datasets comprising 587 whole slide images (WSI) are used to train an ensemble of ten InceptionV3 models whose consensus is used to determine the presence of IC. A novel visualisation strategy was developed to communicate en

SUBMITTER: Solorzano L 

PROVIDER: S-EPMC11252882 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature