光学相干层析成像
卷积神经网络
乳腺癌
人工智能
计算机科学
深度学习
图像分辨率
分辨率(逻辑)
光学层析成像
光学
金标准(测试)
连贯性(哲学赌博策略)
医学物理学
医学
作者
Rohan Bareja,Diana Mojahed,Hanina Hibshoosh,Christine Hendon
出处
期刊:Applied Optics
[The Optical Society]
日期:2022-05-02
卷期号:61 (15): 4458-4458
摘要
Optical coherence tomography (OCT) is being investigated in breast cancer diagnostics as a real-time histology evaluation tool. We present a customized deep convolutional neural network (CNN) for classification of breast tissues in OCT B-scans. Images of human breast samples from mastectomies and breast reductions were acquired using a custom ultrahigh-resolution OCT system with 2.72 µm axial resolution and 5.52 µm lateral resolution. The network achieved 96.7% accuracy, 92% sensitivity, and 99.7% specificity on a dataset of 23 patients. The usage of deep learning will be important for the practical integration of OCT into clinical practice.
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