Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays

Abstract

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.

arXiv comment10 pages. Accepted by the ACM BCB 2018

References in corpus (14)

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
• Fully Convolutional Networks for Semantic Segmentation
• Squeeze-and-Excitation Networks
• Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge
• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• Detecting Cancer Metastases on Gigapixel Pathology Images
• Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification
• Learning to diagnose from scratch by exploiting dependencies among labels
• Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks
• Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions
• Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks
• Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks

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• Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rays
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• Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies
• Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs