Classifying breast cancer in ultrahigh-resolution optical coherence tomography images using convolutional neural networks.
Appl Opt
; 61(15): 4458-4462, 2022 May 20.
Article
em En
| MEDLINE
| ID: mdl-36256284
ABSTRACT
Optical coherence tomography (OCT) is being investigated in breast cancer diagnostics as a real-time histology evaluation tool. We present a customized deep convolutional neural network (CNN) for classification of breast tissues in OCT B-scans. Images of human breast samples from mastectomies and breast reductions were acquired using a custom ultrahigh-resolution OCT system with 2.72 µm axial resolution and 5.52 µm lateral resolution. The network achieved 96.7% accuracy, 92% sensitivity, and 99.7% specificity on a dataset of 23 patients. The usage of deep learning will be important for the practical integration of OCT into clinical practice.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
/
Tomografia de Coerência Óptica
Limite:
Female
/
Humans
Idioma:
En
Revista:
Appl Opt
Ano de publicação:
2022
Tipo de documento:
Article