Deep Learning for Chest X-ray Analysis: A Survey
arXiv:2103.08700 · doi:10.1016/j.media.2021.102125
Abstract
Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Commercially available applications are detailed, and a comprehensive discussion of the current state of the art and potential future directions are provided.
Under review in Medical Image Analysis
References in corpus (38)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- A Survey on Deep Learning in Medical Image Analysis
- Automated Design of Deep Learning Methods for Biomedical Image Segmentation
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
- YOLOv3: An Incremental Improvement
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- How transferable are features in deep neural networks?
- Deep Visual Domain Adaptation: A Survey
- Conditional Image Synthesis With Auxiliary Classifier GANs
- Generative Adversarial Network in Medical Imaging: A Review
- Confounding variables can degrade generalization performance of radiological deep learning models
- COVID-19 Image Data Collection: Prospective Predictions Are the Future
- PadChest: A large chest x-ray image dataset with multi-label annotated reports
- Deep Learning is Robust to Massive Label Noise
- Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation
- Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
- Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations
- Learning to detect chest radiographs containing lung nodules using visual attention networks
- Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis
- Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system
- SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images
- Image to Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography
- BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients
- Learning Deformable Registration of Medical Images with Anatomical Constraints
- XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation
- On the limits of cross-domain generalization in automated X-ray prediction
- Automatic catheter detection in pediatric X-ray images using a scale-recurrent network and synthetic data
- Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays
- X-ray In-Depth Decomposition: Revealing The Latent Structures
- Vispi: Automatic Visual Perception and Interpretation of Chest X-rays
- Looking in the Right place for Anomalies: Explainable AI through Automatic Location Learning
- Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation
- Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays
- FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis
- Do Lateral Views Help Automated Chest X-ray Predictions?
- Automated Identification of Thoracic Pathology from Chest Radiographs with Enhanced Training Pipeline
- Sequential Rib Labeling and Segmentation in Chest X-Ray using Mask R-CNN
- Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields
Cited by in corpus (22)
- Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders
- Deep Learning for Computational Cytology: A Survey
- AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
- Risk of Bias in Chest Radiography Deep Learning Foundation Models
- Exploring scalable medical image encoders beyond text supervision
- A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron
- Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images
- Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey
- Explainable Knowledge Distillation for On-device Chest X-Ray Classification
- Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs
- Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations
- SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction
- Gravity Network for end-to-end small lesion detection
- Artificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images
- Tips and Tricks to Improve CNN-based Chest X-ray Diagnosis: A Survey
- POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 Detection
- Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes
- Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methods
- Cascading Neural Network Methodology for Artificial Intelligence-Assisted Radiographic Detection and Classification of Lead-Less Implanted Electronic Devices within the Chest
- Toward Non-Invasive Diagnosis of Bankart Lesions with Deep Learning
- OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs
- Auto-outlier Fusion Technique for Chest X-ray classification with Multi-head Attention Mechanism