论文标题
使用对抗性神经网络鉴定宫颈病理
Identification of Cervical Pathology using Adversarial Neural Networks
论文作者
论文摘要
各种筛查和诊断方法导致发达国家的宫颈癌死亡率大大降低。但是，宫颈癌是印度和其他低收入国家（LMIC）尤其是在城市贫困人口和贫民窟居民中，宫颈癌与癌症相关的主要原因。几种复杂的技术，例如细胞学测试，HPV测试等，已广泛用于筛查宫颈癌。这些测试本质上很耗时。在本文中，我们提出了一个基于卷积自动编码器的框架，具有类似于Segnet的架构，该体系结构以对抗性方式训练，以对使用阴道镜获得的子宫颈图像进行分类。我们在Intel-Mobile ODT宫颈图像分类数据集上验证性能。该方法的表现优于在图像网数据库中预先训练的微调卷积神经网络的标准技术，平均精度为73.75％。
Various screening and diagnostic methods have led to a large reduction of cervical cancer death rates in developed countries. However, cervical cancer is the leading cause of cancer related deaths in women in India and other low and middle income countries (LMICs) especially among the urban poor and slum dwellers. Several sophisticated techniques such as cytology tests, HPV tests etc. have been widely used for screening of cervical cancer. These tests are inherently time consuming. In this paper, we propose a convolutional autoencoder based framework, having an architecture similar to SegNet which is trained in an adversarial fashion for classifying images of the cervix acquired using a colposcope. We validate performance on the Intel-Mobile ODT cervical image classification dataset. The proposed method outperforms the standard technique of fine-tuning convolutional neural networks pre-trained on ImageNet database with an average accuracy of 73.75%.
