Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning

Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning

Abstract Convolutional neural networks (CNNs) offer the potential to generate comprehensive quantitative analysis of histologic features. Diagnostic reporting of benign breast disease (BBD) biopsies is usually limited to subjective assessment of the most severe lesion in a sample, while ignoring the...

Full description

Bibliographic Details
Main Authors: Thomas de Bel, Geert Litjens, Joshua Ogony, Melody Stallings-Mann, Jodi M. Carter, Tracy Hilton, Derek C. Radisky, Robert A. Vierkant, Brendan Broderick, Tanya L. Hoskin, Stacey J. Winham, Marlene H. Frost, Daniel W. Visscher, Teresa Allers, Amy C. Degnim, Mark E. Sherman, Jeroen A. W. M. van der Laak
Format: Article
Language:English
Published: Nature Portfolio 2022-01-01
Series:npj Breast Cancer
Online Access:https://doi.org/10.1038/s41523-021-00378-7

Internet

https://doi.org/10.1038/s41523-021-00378-7

Similar Items