One-class machine learning classification of skin tissue based on manually scanned optical coherence tomography imaging. Liu, X., Ouellette, S., Jamgochian, M., Liu, Y., & Rao, B. Scientific Reports, 13(1):867, January, 2023. doi abstract bibtex We investigated a method for automatic skin tissue characterization based on optical coherence tomography (OCT) imaging. We developed a manually scanned single fiber OCT instrument to perform in vivo skin imaging and tumor boundary assessment. The goal is to achieve more accurate tissue excision in Mohs micrographic surgery (MMS) and reduce the time required for MMS. The focus of this study was to develop a novel machine learning classification method to automatically identify abnormal skin tissues through one-class classification. We trained a deep convolutional neural network (CNN) with a U-Net architecture for automatic skin segmentation, used the pre-trained U-Net as a feature extractor, and trained one-class support vector machine (SVM) classifiers to detect abnormal tissues. The novelty of this study is the use of a neural network as a feature extractor and the use of a one-class SVM for abnormal tissue detection. Our approach eliminated the need to engineer the features for classification and eliminated the need to train the classifier with data obtained from abnormal tissues. To validate the effectiveness of the one-class classification method, we assessed the performance of our algorithm using computer synthesized data, and experimental data. We also performed a pilot study on a patient with skin cancer.
@article{liu_one-class_2023,
title = {One-class machine learning classification of skin tissue based on manually scanned optical coherence tomography imaging},
volume = {13},
issn = {2045-2322},
doi = {10.1038/s41598-023-28155-5},
abstract = {We investigated a method for automatic skin tissue characterization based on optical coherence tomography (OCT) imaging. We developed a manually scanned single fiber OCT instrument to perform in vivo skin imaging and tumor boundary assessment. The goal is to achieve more accurate tissue excision in Mohs micrographic surgery (MMS) and reduce the time required for MMS. The focus of this study was to develop a novel machine learning classification method to automatically identify abnormal skin tissues through one-class classification. We trained a deep convolutional neural network (CNN) with a U-Net architecture for automatic skin segmentation, used the pre-trained U-Net as a feature extractor, and trained one-class support vector machine (SVM) classifiers to detect abnormal tissues. The novelty of this study is the use of a neural network as a feature extractor and the use of a one-class SVM for abnormal tissue detection. Our approach eliminated the need to engineer the features for classification and eliminated the need to train the classifier with data obtained from abnormal tissues. To validate the effectiveness of the one-class classification method, we assessed the performance of our algorithm using computer synthesized data, and experimental data. We also performed a pilot study on a patient with skin cancer.},
language = {eng},
number = {1},
journal = {Scientific Reports},
author = {Liu, Xuan and Ouellette, Samantha and Jamgochian, Marielle and Liu, Yuwei and Rao, Babar},
month = jan,
year = {2023},
pmid = {36650283},
pmcid = {PMC9845382},
keywords = {Humans, Machine Learning, Neural Networks, Computer, Pilot Projects, Skin, Support Vector Machine, Tomography, Optical Coherence},
pages = {867},
}
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The focus of this study was to develop a novel machine learning classification method to automatically identify abnormal skin tissues through one-class classification. We trained a deep convolutional neural network (CNN) with a U-Net architecture for automatic skin segmentation, used the pre-trained U-Net as a feature extractor, and trained one-class support vector machine (SVM) classifiers to detect abnormal tissues. The novelty of this study is the use of a neural network as a feature extractor and the use of a one-class SVM for abnormal tissue detection. Our approach eliminated the need to engineer the features for classification and eliminated the need to train the classifier with data obtained from abnormal tissues. To validate the effectiveness of the one-class classification method, we assessed the performance of our algorithm using computer synthesized data, and experimental data. 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