Journal
BIOMEDICAL OPTICS EXPRESS
Volume 13, Issue 6, Pages 3380-3400Publisher
Optica Publishing Group
DOI: 10.1364/BOE.455110
Keywords
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Funding
- Australian Research Council
- Department of Health, Government of Western Australia
- Cancer Council Western Australia
- University of Western Australia
- Australian Government Research Training Program (RTP) Scholarship
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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.
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
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