Multi-class classification of breast tissue using optical coherence tomography and attenuation imaging combined via deep learning
Ken Y. Foo, Kyle Newman, Q. Fang, Peijun Gong, Hina Ismail, Devina D. Lakhiani, Renate Zilkens, Benjamin F. Dessauvagie, Bruce Latham, Christobel Saunders, Lixin Chin, Brendan F. Kennedy
Abstract
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.