Thyroid Cytopathology Cancer Diagnosis from Smartphone Images Using Machine Learning

Wilson, 2018, Access to pathology and laboratory medicine services: a crucial gap, Lancet, 391, 1927, 10.1016/S0140-6736(18)30458-6 Campanella, 2019, Clinical-grade computational pathology using weakly supervised deep learning on whole slide images, Nat Med, 25, 1301, 10.1038/s41591-019-0508-1 Dov, 2021, Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images, Med Image Anal, 67, 10.1016/j.media.2020.101814 Nagpal, 2019, Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer, NPJ Digit Med, 2, 48, 10.1038/s41746-019-0112-2 Isse, 2012, Digital transplantation pathology: combining whole slide imaging, multiplex staining and automated image analysis, Am J Transplant, 12, 27, 10.1111/j.1600-6143.2011.03797.x Cibas, 2017, The 2017 Bethesda system for reporting thyroid cytopathology, Thyroid, 27, 1341, 10.1089/thy.2017.0500 Simonyan Sandler, 2018, Mobilenetv2: Inverted residuals and linear bottlenecks, 4510 Lin M, Chen Q, Yan S. Network in network. Preprint. Published online Dec 16, 2013. arXiv:13124400. https://doi.org/10.48550/arXiv.1312.4400 Loshchilov I, Hutter F. Decoupled weight decay regularization. Preprint. Published online Nov 14, 2017. arXiv:171105101. https://doi.org/10.48550/arXiv.1711.05101 Dietterich, 2000, Ensemble Methods in Machine Learning, 1857 DeLong, 1988, Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 837, 10.2307/2531595 Shorten, 2019, A survey on image data augmentation for deep learning, J Big Data, 6, 1 Müller SG, Hutter F. Trivialaugment: tuning-free yet state-of-the-art data augmentation. Preprint. Published online Mar 18, 2021. arXiv:2103.10158v2. https://doi.org/10.48550/arXiv.2103.10158 Sanyal, 2018, Artificial intelligence in cytopathology: a neural network to identify papillary carcinoma on thyroid fine-needle aspiration cytology smears, J Pathol Inform, 9, 43, 10.4103/jpi.jpi_43_18 Savala, 2018, Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid, Diagn Cytopathol, 46, 244, 10.1002/dc.23880 Guan, 2019, Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study, J Cancer, 10, 4876 Kezlarian, 2021, Artificial intelligence in thyroid fine needle aspiration biopsies, Acta Cytol, 65, 324, 10.1159/000512097 Skandarajah, 2014, Quantitative imaging with a mobile phone microscope, PLoS One, 9, 10.1371/journal.pone.0096906 Rivenson, 2018, Deep learning enhanced mobile-phone microscopy, ACS Photonics, 5, 2354, 10.1021/acsphotonics.8b00146 de Haan, 2020, Automated screening of sickle cells using a smartphone-based microscope and deep learning, NPJ Digit Med, 3, 1, 10.1038/s41746-020-0282-y Faquin, 2016, Impact of reclassifying noninvasive follicular variant of papillary thyroid carcinoma on the risk of malignancy in The Bethesda System for Reporting Thyroid Cytopathology, Cancer Cytopathol, 124, 181, 10.1002/cncy.21631 Elliott Range, 2020, Application of a machine learning algorithm to predict malignancy in thyroid cytopathology, Cancer Cytopathol, 128, 287, 10.1002/cncy.22238 Haugen, 2016, 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American Thyroid Association guidelines task force on thyroid nodules and differentiated thyroid cancer, Thyroid, 26, 1, 10.1089/thy.2015.0020