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

Posted on 12.05.2022 - 12:39
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.

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Foo, Ken; Newman, Kyle; Fang, Qi; Gong, Peijun; Ismail, Hina; Lakhiani, Devina; et al. (2022): Multi-class classification of breast tissue using optical coherence tomography and attenuation imaging combined via deep learning. Optica Publishing Group. Collection. https://doi.org/10.6084/m9.figshare.c.5964672.v2
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AUTHORS (10)

Ken Foo
Kyle Newman
Qi Fang
Peijun Gong
Hina Ismail
Devina Lakhiani
Renate Zilkens
Benjamin Dessauvagie
Bruce Latham
Christobel Saunders

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