Open AccessMulti-class classification of breast tissue using optical coherence tomography and attenuation imaging combined via deep learning
Author(s) -
Ken Y. Foo,
Kyle Newman,
Qi Fang,
Peijun Gong,
Hina Ismail,
Devina D. Lakhiani,
Renate Zilkens,
Benjamin F. Dessauvagie,
Bruce Latham,
Christobel Saunders,
Lixin Chin,
Brendan F. Kennedy
Publication year - 2022
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.455110
Subject(s) - optical coherence tomography , attenuation , convolutional neural network , artificial intelligence , attenuation coefficient , breast tissue , medicine , computer science , nuclear medicine , radiology , breast cancer , optics , physics , cancer
We demonstrate a convolutional neural network (CNN) for multi-class breast tissue classification as adipose tissue, benign dense tissue, or malignant tissue, using multi-channel optical coherence tomography (OCT) and attenuation images, and a novel Matthews correlation coefficient (MCC)-based loss function that correlates more strongly with performance metrics than the commonly used cross-entropy loss. We hypothesized that using multi-channel images would increase tumor detection performance compared to using OCT alone. 5,804 images from 29 patients were used to fine-tune a pre-trained ResNet-18 network. Adding attenuation images to OCT images yields statistically significant improvements in several performance metrics, including benign dense tissue sensitivity (68.0% versus 59.6%), malignant tissue positive predictive value (PPV) (79.4% versus 75.5%), and total accuracy (85.4% versus 83.3%), indicating that the additional contrast from attenuation imaging is most beneficial for distinguishing between benign dense tissue and malignant tissue.