论文标题
使用组织病理学图像检测乳腺癌
Breast Cancer Detection using Histopathological Images
论文作者
论文摘要
癌症是世界上最常见和致命的疾病之一。乳腺癌每八名女性中有一个，每800名女性中有一个。因此，我们的主要目标应该是对癌症的早期检测，因为癌症的早期发现有助于有效治愈癌症。因此，我们在先进的深度学习技术的帮助下提出了一个显着性检测系统，以便将教授机器模仿病理学家在诊断相关区域的定位方面的作用。我们通过训练CNN（VGG16，Resnet架构）研究了五个诊断类别的乳腺癌诊断。我们已经使用Breakhis数据集训练我们的模型。我们专注于组织病理学图像中癌区域的检测和分类。诊断相关的区域是显着的。检测系统将作为开源Web应用程序可用，可以由病理学家和医疗机构使用。
Cancer is one of the most common and fatal diseases in the world. Breast cancer affects one in every eight women and one in every eight hundred men. Hence, our prime target should be early detection of cancer because the early detection of cancer can be helpful to cure cancer effectively. Therefore, we propose a saliency detection system with the help of advanced deep learning techniques, such that the machine will be taught to emulate actions of pathologists for localization of diagnostically pertinent regions. We study identification of five diagnostic categories of breast cancer by training a CNN (VGG16, ResNet architecture). We have used BreakHis dataset to train our model. We focus on both detection and classification of cancerous regions in histopathology images. The diagnostically relevant regions are salient. The detection system will be available as an open source web application which can be used by pathologists and medical institutions.
