Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations  

作　　者：Erica Skerrett Zichen Miao Mercy N.Asiedu Megan Richards Brian Crouch Guillermo Sapiro Qiang Qiu Nirmala Ramanujam 

机构地区：[1]Department of Biomedical Engineering,Duke University,Durham,NC,USA [2]Department of Electrical and Computer Engineering,Purdue University,West Lafayette,IN,USA [3]Department of Computer Engineering,Massachusetts Institute of Technology,Cambridge,MA,USA [4]Department of Electrical and Computer Engineering,Department of Biomedical Engineering,Department of Computer Science,Department of Mathematics,Duke University,Durham,NC,USA

出　　处：《Biomedical Engineering Frontiers》2022年第1期381-393,共13页生物医学工程前沿（英文）

摘　　要：Objective and Impact Statement.We use deep learning models to classify cervix images—collected with a low-cost,portable Pocket colposcope—with biopsy-confirmed high-grade precancer and cancer.We boost classification performance on a screened-positive population by using a class-balanced loss and incorporating green-light colposcopy image pairs,which come at no additional cost to the provider.Introduction.Because the majority of the 300,000 annual deaths due to cervical cancer occur in countries with low-or middle-Human Development Indices,an automated classification algorithm could overcome limitations caused by the low prevalence of trained professionals and diagnostic variability in provider visual interpretations.Methods.Our dataset consists of cervical images(n=1,760)from 880 patient visits.After optimizing the network architecture and incorporating a weighted loss function,we explore two methods of incorporating green light image pairs into the network to boost the classification performance and sensitivity of our model on a test set.Results.We achieve an area under the receiver-operator characteristic curve,sensitivity,and specificity of 0.87,75%,and 88%,respectively.The addition of the class-balanced loss and green light cervical contrast to a Resnet-18 backbone results in a 2.5 times improvement in sensitivity.Conclusion.Our methodology,which has already been tested on a prescreened population,can boost classification performance and,in the future,be coupled with Pap smear or HPV triaging,thereby broadening access to early detection of precursor lesions before they advance to cancer.

关 键 词：boost network BACKBONE 

分 类 号：R737.33[医药卫生—肿瘤]