Application of deep learning to segmentation and grading of fine needle biopsy breast images

癌症治疗的有效性在很大程度上取决于疾病的早期发现。重要的和经常使用的诊断方法是X光、CAT扫描、MRI扫描和超声成像。一旦发现癌症的早期迹象，医生通常建议对可疑肿块进行活检。对于许多类型的癌症，包括乳腺癌和肝癌，细针抽吸活组织检查(FNB)是一种首选的诊断技术，因为它比开放和闭合手术活检侵袭性小。从FNB图像确定癌症级别可能是一项具有挑战性的任务，即使对病理学家来说也是如此，因为吸入过程可能导致组织结构的部分破坏，有时甚至是细胞核的破坏。机器学习和高性能数据计算的最新进展为高精度医学图像的智能处理创造了新的可能性。特别是，深度卷积神经网络已经证明了识别图像中各种对象的能力，并且可以超越最先进的对象识别技术。这项拟议的研究旨在利用ORS公司开发的计算机专家成像软件创建一种创新的计算系统，用于快速可靠地进行癌症分级，该软件用于使用深度卷积神经网络和最先进的随机森林分类器对虚拟FNB切片进行自动分割和分级。

The effectiveness of cancer treatment largely depends on the early detection of the disease. Important and often used diagnostic methods are x rays, cat scans, MRI scans and ultrasonography imaging. Once early signs of cancer are detected, doctors often recommend biopsy of suspicious mass. For many types of cancers, including breast cancer and liver cancer, fine needle aspiration biopsy (FNB) is a preferred diagnostic technique as it is less invasive than the open and closed surgical biopsy. Determining cancer grade from FNB images may be a challenging task even for pathologists since aspiration process may result in partial destruction of the tissue structure and sometimes even of nuclei. Recent advances in machine learning and high performance data computation have created new possibilities for intelligent processing of medical images with high accuracy. In particular, deep convolutional neural networks have demonstrated the capacity to recognize various objects within an image, and can outperform state-of-the-art object recognition techniques. The proposed research aims at creating an innovative computational system for rapid and reliable cancer grading utilizing computer-expert imaging software developed by the ORS company for automatic segmentation and grading of virtual FNB slides using deep convolutional neural networks combined with the state-of-the-art random forest classifiers.