论文标题
基于CNN的基于CNN的宫颈癌分类方法在全术组织病理学图像中
CNN-based Approach for Cervical Cancer Classification in Whole-Slide Histopathology Images
论文作者
论文摘要
到2040年，宫颈癌每年将导致460 000人死亡，大约90％是撒哈拉以南非洲妇女。非洲不断增加的发病率使宫颈癌成为世界卫生组织（WHO）在筛查，诊断和治疗方面的优先事项。通常，癌症的诊断主要依赖于组织病理学评估，这是一种非常容易出错的程序，需要智能计算机辅助系统作为低成本的患者安全机制，但在数字病理学中缺乏标记的数据限制了其适用性。在这项研究中，很少对TCGA数据门户的宫颈组织数字载玻片进行预处理以克服全扫描图像障碍，并包括我们提出的VGG16-CNN分类方法中。我们的结果达到了98,26％的准确性，F1得分为97,9％，这证实了在这项弱监督任务上转移学习的潜力。
Cervical cancer will cause 460 000 deaths per year by 2040, approximately 90% are Sub-Saharan African women. A constantly increasing incidence in Africa making cervical cancer a priority by the World Health Organization (WHO) in terms of screening, diagnosis, and treatment. Conventionally, cancer diagnosis relies primarily on histopathological assessment, a deeply error-prone procedure requiring intelligent computer-aided systems as low-cost patient safety mechanisms but lack of labeled data in digital pathology limits their applicability. In this study, few cervical tissue digital slides from TCGA data portal were pre-processed to overcome whole-slide images obstacles and included in our proposed VGG16-CNN classification approach. Our results achieved an accuracy of 98,26% and an F1-score of 97,9%, which confirm the potential of transfer learning on this weakly-supervised task.
