Spatially Preserving Flattening For Location-Aware Classification Of Findings In Chest X-Rays

Chest X-rays have become the focus of vigorous deep learning research in recent years due to availability of large labeled datasets. While classification of anomalous findings is now possible, ensuring that they are correctly localized still remains challenging as it 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 autoencoding of feature maps during flattening. The feature maps, autoencoder 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 findings classification over state-of-the-art methods without requiring detailed anatomy segmentation or large-scale region annotation.