Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

ABSTRACT:

Importance

Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency.

Objective

Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting.

Design, setting, and participants

Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge par

SUBMITTER: Ehteshami Bejnordi B 

PROVIDER: S-EPMC5820737 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature