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Automated pulmonary nodule detection using 3D deep convolutional neural networks

机译：使用3D深层卷积神经网络自动进行肺结节检测

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Early detection of pulmonary nodules in computed tomography (CT) images is essential for successful outcomes among lung cancer patients. Much attention has been given to deep convolutional neural network (DCNN)-based approaches to this task, but models have relied at least partly on 2D or 2.5D components for inherently 3D data. In this paper, we introduce a novel DCNN approach, consisting of two stages, that is fully three-dimensional end-to-end and utilizes the state-of-the-art in object detection. First, nodule candidates are identified with a U-Net-inspired 3D Faster R-CNN trained using online hard negative mining. Second, false positive reduction is performed by 3D DCNN classifiers trained on difficult examples produced during candidate screening. Finally, we introduce a method to ensemble models from both stages via consensus to give the final predictions. By using this framework, we ranked first of 2887 teams in Season One of Alibaba's 2017 TianChi AI Competition for Healthcare.
机译：在计算机断层扫描（CT）图像中及早发现肺结节对于肺癌患者的成功结局至关重要。对于基于深度卷积神经网络（DCNN）的方法，已经给予了很多关注，但是对于固有的3D数据，模型至少部分依赖于2D或2.5D分量。在本文中，我们介绍了一种新颖的DCNN方法，该方法包括两个阶段，这是完全端到端的三维过程，并利用最新技术进行对象检测。首先，通过在线硬负采矿训练的受U-Net启发的3D Faster R-CNN识别结节候选物。其次，由3D DCNN分类器对候选筛选过程中产生的困难示例进行训练，从而减少误报。最后，我们介绍了一种通过共识对两个阶段的模型进行集成的方法，以给出最终的预测。通过使用此框架，我们在阿里巴巴2017天池AI医疗竞赛第一季的2887个团队中排名第一。

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