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Large scale deep learning for computer aided detection of mammographic lesions

机译：计算机辅助检测乳房XUSION的大规模深度学习

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摘要

Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object detection and speech recognition. In this paper we provide a head-to-head comparison between a state-of-the art in mammography CAD system, relying on a manually designed feature set and a Convolutional Neural Network (CNN), aiming for a system that can ultimately read mammograms independently. Both systems are trained on a large data set of around 45,000 images and results show the CNN outperforms the traditional CAD system at low sensitivity and performs comparable at high sensitivity. We subsequently investigate to what extent features such as location and patient information and commonly used manual features can still complement the network and see improvements at high specificity over the CNN especially with location and context features, which contain information not available to the CNN. Additionally, a reader study was performed, where the network was compared to certified screening radiologists on a patch level and we found no significant difference between the network and the readers. (C) 2016 Elsevier B.V. All rights reserved.
机译：机器学习的最新进展产生了培训深层神经网络的新技术，这导致了在许多模式识别任务之类的对象检测和语音识别中的高度成功应用。在本文中，我们在乳房X线摄影系统中的最新技术之间提供了头部比较，依赖于手动设计的功能集和卷积神经网络（CNN），旨在最终读取乳房X光图的系统独立。两个系统在大约45,000个图像的大型数据集上培训，结果显示CNN以低灵敏度低于传统CAD系统，并且在高灵敏度下进行可比性。我们随后调查诸如位置和患者信息的程度和常用的手动功能仍然可以补充网络，并在CNN上看到高特异性的改进，特别是在CNN中包含不可用的信息。另外，进行了读者研究，其中将网络与经过认证的筛选放射科学家进行比较，我们发现网络与读者之间没有显着差异。 （c）2016年Elsevier B.v.保留所有权利。

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