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5 March 2021 Detection of cervical dysplasia from depth-resolved light scattering profiles using deep learning
Haoran Zhang, Wesley Y. Kendall, Evan T. Jelly, Adam Wax
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Proceedings Volume 11657, Biomedical Applications of Light Scattering XI; 116570I (2021) https://doi.org/10.1117/12.2579912
Event: SPIE BiOS, 2021, Online Only
Abstract
We present a machine learning method for the detection and staging of cervical dysplasia tissue using a convolutional neural network (CNN)-based architecture. Depth-resolved angular scattering measurements collected from two clinical trials consisting of 6660 and 1600 depth scans were used as training and validation sets separately. Our results demonstrated high sensitivity and specificity for classifying cervical dysplasia at a hundredfold faster processing time compared with the traditional Mie-theory inverse light scattering analysis (ILSA) method, offering a promising approach for a/LCI in the clinic for assessing cervical dysplasia.
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Haoran Zhang, Wesley Y. Kendall, Evan T. Jelly, and Adam Wax "Detection of cervical dysplasia from depth-resolved light scattering profiles using deep learning", Proc. SPIE 11657, Biomedical Applications of Light Scattering XI, 116570I (5 March 2021); https://doi.org/10.1117/12.2579912
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KEYWORDS
Light scattering
Clinical trials
Tissue optics
Data processing
Databases
Interferometry
Mie scattering
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