Skerrett Erica, Miao Zichen, Asiedu Mercy N, Richards Megan, Crouch Brian, Sapiro Guillermo, Qiu Qiang, Ramanujam Nirmala
Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA.
BME Front. 2022 Aug 25;2022:9823184. doi: 10.34133/2022/9823184. eCollection 2022.
. 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. . 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. . Our dataset consists of cervical images () 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. . 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. . 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.
我们使用深度学习模型对通过低成本便携式袖珍阴道镜收集的宫颈图像进行分类，这些图像经活检确诊为高级别癌前病变和癌症。我们通过使用类平衡损失并纳入无需提供者额外付费的绿灯阴道镜图像对，提高了筛查呈阳性人群的分类性能。由于每年因宫颈癌导致的30万例死亡中，大多数发生在人类发展指数低或中等的国家，一种自动分类算法可以克服因训练有素的专业人员数量少以及提供者视觉解读中的诊断变异性所造成的限制。我们的数据集由来自880次患者就诊的宫颈图像组成。在优化网络架构并纳入加权损失函数后，我们探索了两种将绿灯图像对纳入网络的方法，以提高我们模型在测试集上的分类性能和敏感性。我们分别实现了受试者工作特征曲线下面积、敏感性和特异性为0.87、75%和88%。在Resnet - 18主干中添加类平衡损失和绿灯宫颈对比度可使敏感性提高2.5倍。我们的方法已经在预筛查人群上进行了测试，可以提高分类性能，并且在未来，可与巴氏涂片或HPV分流相结合，从而在癌前病变发展为癌症之前扩大早期检测的可及性。
