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.

本研究基于自动编码训练出一种新的深度学习神经网络，能够保留解剖结构和空间位置信息，通过对胸部X光片的异常评估，实现精确定位和分类，相较现有方法有了显著提升。

用于胸部 X 光片结果定位分类的空间保留平面化技术