Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays
This work addresses the problem of accurate disease detection in chest X-rays for clinical practice, but it is incremental as it builds on existing weakly supervised approaches with specific architectural improvements.
The authors tackled the challenge of classifying and localizing thoracic diseases on chest X-rays using a weakly supervised deep learning framework, achieving better performance against state-of-the-art methods on the ChestX-ray14 dataset.
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.