ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography

Abstract

Computer-aided techniques may lead to more accurate and more acces-sible diagnosis of thorax diseases on chest radiography. Despite the success of deep learning-based solutions, this task remains a major challenge in smart healthcare, since it is intrinsically a weakly supervised learning problem. In this paper, we incorporate the attention mechanism into a deep convolutional neural network, and thus propose the ChestNet model to address effective diagnosis of thorax diseases on chest radiography. This model consists of two branches: a classification branch serves as a uniform feature extraction-classification network to free users from troublesome handcrafted feature extraction, and an attention branch exploits the correlation between class labels and the locations of patholog-ical abnormalities and allows the model to concentrate adaptively on the patholog-ically abnormal regions. We evaluated our model against three state-of-the-art deep learning models on the Chest X-ray 14 dataset using the official patient-wise split. The results indicate that our model outperforms other methods, which use no extra training data, in diagnosing 14 thorax diseases on chest radiography.

8 pages, 3 figures

References in corpus (8)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• A Survey on Deep Learning in Medical Image Analysis
• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• Multiple Object Recognition with Visual Attention
• Learning to diagnose from scratch by exploiting dependencies among labels
• Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions
• Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification
• Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks

Cited by in corpus (14)

• CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
• Image to Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography
• A Survey on Deep Learning of Small Sample in Biomedical Image Analysis
• Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels
• A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays
• Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images
• A free web service for fast COVID-19 classification of chest X-Ray images
• Artificial Intelligence for COVID-19 Detection -- A state-of-the-art review
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks
• Transfer-Learning-Aware Neuro-Evolution for Diseases Detection in Chest X-Ray Images
• Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies
• Understanding Human Innate Immune System Dependencies using Graph Neural Networks
• Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs
• Learning Visual Context by Comparison