Multi-class classification of breast tissue using optical coherence tomography and attenuation imaging combined via deep learning

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

Automated summary

In this study, a convolutional neural network (CNN) was used for multi-class breast tissue classification using multi-channel optical coherence tomography (OCT) and attenuation images. The researchers introduced a novel loss function based on the Matthews correlation coefficient (MCC) to better correlate with performance metrics. The results showed that adding attenuation images to OCT images significantly improved the performance of breast tissue classification.