Spatially-Preserving Flattening for Location-Aware Classification of Findings in Chest X-Rays

TL;DR

This paper introduces a novel deep learning architecture that preserves spatial information during feature flattening, significantly improving location-aware classification of findings in chest X-rays.

Contribution

A new spatially-preserving deep learning network that maintains location and shape information through auto-encoding during flattening, enhancing classification accuracy.

Findings

Significant improvement over state-of-the-art methods.

Effective localization of anomalies in chest X-rays.

Validated on a large multi-hospital dataset.

Abstract

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.