  Chest X-rays is one of the most commonly available and affordable
radiological examinations in clinical practice. While detecting thoracic
diseases on chest X-rays is still a challenging task for machine intelligence,
due to 1) the highly varied appearance of lesion areas on X-rays from patients
of different thoracic disease and 2) the shortage of accurate pixel-level
annotations by radiologists for model training. Existing machine learning
methods are unable to deal with the challenge that thoracic diseases usually
happen in localized disease-specific areas. In this article, we propose a
weakly supervised deep learning framework equipped with squeeze-and-excitation
blocks, multi-map transfer, and max-min pooling for classifying thoracic
diseases as well as localizing suspicious lesion regions. The comprehensive
experiments and discussions are performed on the ChestX-ray14 dataset. Both
numerical and visual results have demonstrated the effectiveness of the
proposed model and its better performance against the state-of-the-art
pipelines.
