论文标题
胸部X射线中发现的位置感知结果分类，以空间保护扁平
Spatially-Preserving Flattening for Location-Aware Classification of Findings in Chest X-Rays
论文作者
论文摘要
近年来，由于大型标记数据集的可用性，胸部X射线已成为近年来大力深度学习研究的重点。尽管现在可以对异常发现的分类进行分类，以确保正确定位它们仍然具有挑战性，因为这需要识别解剖区域内的异常情况。现有用于细粒度异常分类的深度学习网络使用体系结构学习特定于位置的发现，其中位置和空间连续性信息在分类前的扁平步骤中丢失了。在本文中，我们提出了一个新的空间保存深度学习网络，该网络通过在扁平化过程中自动编码特征地图来保留位置和塑造信息。然后以端到端的方式对功能地图，自动编码器和分类器进行培训，以使位置意识到胸部X射线检查结果的分类。结果显示在大型多医院X射线数据集中，表明发现分类质量对最先进方法的质量有显着提高。
Chest X-rays have become the focus of vigorous deep learning research in recent years due to the availability of large labeled datasets. While classification of anomalous findings is now possible, ensuring that they are correctly localized still remains challenging, as this requires recognition of anomalies within anatomical regions. Existing deep learning networks for fine-grained anomaly classification learn location-specific findings using architectures where the location and spatial contiguity information is lost during the flattening step before classification. In this paper, we present a new spatially preserving deep learning network that preserves location and shape information through auto-encoding of feature maps during flattening. The feature maps, auto-encoder and classifier are then trained in an end-to-end fashion to enable location aware classification of findings in chest X-rays. Results are shown on a large multi-hospital chest X-ray dataset indicating a significant improvement in the quality of finding classification over state-of-the-art methods.
