A Novel Approach for Tuberculosis Screening Based on Deep Convolutional Neural Networks

Learning Visual Context by Comparison

SRM: A Style-based Recalibration Module for Convolutional Neural Networks

Learning Loss for Active Learning

PseudoEdgeNet: Nuclei Segmentation only with Point Annotations

Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks

Distort-and-Recover: Color Enhancement Using Deep Reinforcement Learning

CBAM: Convolutional Block Attention Module

BAM: Bottleneck Attention Module

Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks

Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks

Transferring Knowledge to Smaller Network With Class-Distance Loss

Semantic Noise Modeling for Better Representation Learning

Self-Transfer Learning for Fully Weakly Supervised Object Localization

Pixel-Level Domain Transfer

Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation

AttentionNet: Aggregating Weak Directions for Accurate Object Detection

Development and Validation of a Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs

Clinical Validation of a Deep Learning Algorithm for Detection of Pneumonia on Chest Radiographs in Emergency Department Patients with Acute Febrile Respiratory Illness

Undetected Lung Cancer at Posteroanterior Chest Radiography: Potential Role of a Deep Learning–based Detection Algorithm

Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study

Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs

Deep-learning Based Automated Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs: Diagnostic Performance in Systematic Screening of Asymptomatic Individuals

Performance of a Deep-learning Algorithm Compared to Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population

Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19

Deep Learning–based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs

Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration

Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study

Test-retest reproducibility of a deep learning–based automatic detection algorithm for the chest radiograph

Deep Learning for Chest Radiograph Diagnosis in the Emergency Department

Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs

Development and Validation of a Deep Learning–based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs

Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs

Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort

Reducing Domain Gap by Reducing Style Bias

AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset

COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system

Deep Learning for Detection of Pulmonary Metastasis on Chest Radiographs