Interstitial lung diseases via deep convolutional neural networks: Segmentation label propagation, unordered pooling and cross-dataset learning

Holistically detecting interstitial lung disease (ILD) patterns from CT images is challenging yet clinically important. Unfortunately, most existing solutions rely on manually provided regions of interest, limiting their clinical usefulness. We focus on two challenges currently existing in two publicly available datasets. First of all, missed labeling of regions of interest is a common issue in existingmedical image datasets due to the labor-intensive nature of the annotation task which requires high levels of clinical proficiency. Second, no work has yet focused on predicting more than one ILD from the same CT slice, despite the frequency of such occurrences. To address these limitations, we propose three algorithms based on deep convolutional neural networks (CNNs). The differences between the two main publicly available datasets are discussed as well.