ArticleBiomedical optics express2022
Multi-class classification of breast tissue using optical coherence tomography and attenuation imaging combined via deep learning.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 20 citations in OpenAlex.
- Label-free diagnosis across the thyroid nodule pathology spectrum using deep learning-enabled optical coherence tomography.Biomedical optics express · 2026Article
- Key Measures for Evaluating Diagnostic Accuracy in Multi-Class Classification: An Overview and Simulation-Based Comparison.Korean journal of radiology · 2026Review
- Development of breast-mimicking phantoms for use in optical coherence elastography.Journal of biomedical optics · 2025Article
- Impact of sample handling protocols on soft tissue attenuation coefficient and morphology.Journal of biomedical optics · 2025Article
- Clinical evaluation of breast cancer tissue with optical coherence tomography: key findings from a large-scale study.Journal of cancer research and clinical oncology · 2025Article
- Accurate attenuation characterization in optical coherence tomography using multi-reference phantoms and deep learning.Biomedical optics express · 2024Article
- Compression OCT-elastography combined with speckle-contrast analysis as an approach to the morphological assessment of breast cancer tissue.Biomedical optics express · 2023Article
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.