  The identification and localization of diseases in medical images using deep
learning models have recently attracted significant interest. Existing methods
only consider training the networks with each image independently and most
leverage an activation map for disease localization. In this paper, we propose
an alternative approach that learns discriminative features among triplets of
images and cyclically trains on region features to verify whether attentive
regions contain information indicative of a disease. Concretely, we adapt a
distance learning framework for multi-label disease classification to
differentiate subtle disease features. Additionally, we feed back the features
of the predicted class-specific regions to a separate classifier during
training to better verify the localized diseases. Our model can achieve
state-of-the-art classification performance on the challenging ChestX-ray14
dataset, and our ablation studies indicate that both distance learning and
region verification contribute to overall classification performance. Moreover,
the distance learning and region verification modules can capture essential
information for better localization than baseline models without these modules.

Computer Vision and Pattern Recognition