Prevention and early diagnosis of breast cancer (BC) is an essential prerequisite for the selection of proper treatment. The substantial pressure due to the increase of demand for faster and more precise diagnostic results drives for automatic solutions. In the past decade, deep learning techniques have demonstrated their power over several domains, and Computer-Aided (CAD) diagnostic became one of them. However, when it comes to the analysis of Whole Slide Images (WSI), most of the existing works compute predictions from levels independently. This is, however, in contrast to the histopathologist expert approach who requires to see a global architecture of tissue structures important in BC classification. We present a deep learning-based solution and framework for processing WSI based on a novel approach utilizing the advantages of image levels. We apply the weighing of information extracted from several levels into the final classification of the malignancy. Our results demonstrate the profitability of global information with an increase of accuracy from 72.2% to 84.8%.
翻译:预防和早期诊断乳腺癌(BC)是选择适当治疗的基本先决条件。由于对更快、更精确的诊断结果驱动自动解决方案的需求增加而带来的巨大压力。在过去的十年中,深层次的学习技术展示了其在若干领域的力量,计算机辅助诊断也成为其中之一。然而,在分析整个幻灯片图像(SSI)时,大多数现有作品独立地从水平上计算预测值。然而,这与病理学家专家的做法不同,后者要求看到一个对不列颠哥伦比亚分类十分重要的组织结构的全球结构。我们提出了一个深层次的学习解决方案和框架,以便利用图像水平的优势,在新颖的方法基础上处理WSI。我们把从若干级别获取的信息的权衡纳入恶性病的最后分类中。我们的结果表明全球信息利润丰厚,准确率从72.2%提高到84.8%。