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CNN-based Method for Lung Cancer Detection in Whole Slide Histopathology Images

机译：基于CNN的肺癌肺癌检测方法中的整个载玻片组织病理学图像

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摘要

Early diagnosis of lung cancer is critical for improvement of patient survival. Histopathological assessment of tissue is standard procedure needed for early diagnosis. Tissue analysis is usually performed by pathologist review, but this procedure is time-consuming and error-prone. Automated detection of cancer regions would significantly speed up the whole process and help the pathologist. In this paper we propose fully automatic method for lung cancer detection in whole slide images of lung tissue samples. Classification is performed on image patch level using convolutional neural network (CNN). Two CNN architectures (VGG and ResNet) are trained and their performance are compared. Obtained results show that CNN based approach has potential to help pathologists in lung cancer diagnosis.
机译：早期诊断肺癌对于改善患者存活至关重要。组织的组织病理学评估是早期诊断所需的标准程序。组织分析通常由病理学家审查进行，但这种程序是耗时和易于出错的。自动检测癌症区将显着加速整个过程并帮助病理学家。本文提出了肺组织样品的整个幻灯片图像中肺癌检测的全自动方法。使用卷积神经网络（CNN）对图像补丁电平进行分类。培训两个CNN架构（VGG和RESET），并比较它们的性能。得到的结果表明，基于CNN的方法有可能帮助肺癌诊断的病理学家。

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