Publications that evaluate CAD4TB
Please also see Diagnostic Image Analysis Group
- of Radboud University Medical Center. The group has its roots in computer-aided detection of breast cancer in mammograms, and expanded to automated detection and diagnosis in breast MRI, ultrasound and tomosynthesis, chest radiographs and chest CT, prostate MRI, neuro-imaging and the analysis of retinal and digital pathology images. The technology used is primarily deep learning
Actual overview of CAD publications as listed at:
Accuracy of an automated system for tuberculosis detection on chest radiographs in high-risk screening.
Automatic versus human reading of chest X-rays in the Zambia National Tuberculosis Prevalence Survey.
An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information.
Screening for pulmonary tuberculosis in a Tanzanian prison and computer-aided interpretation of chest X-rays.
Automated chest-radiography as a triage for Xpert® testing in resource-constrained settings: a prospective study of diagnostic accuracy and costs.
Computerized Reading of Chest Radiographs in The Gambia National Tuberculosis Prevalence Survey: Retrospective Comparison with Human Experts.
Objective Computerized Chest Radiography Screening to Detect Tuberculosis in the Philippines.
Diagnostic accuracy of computer-aided detection of pulmonary tuberculosis in chest radiographs: a validation study from sub-saharan Africa.
The Sensitivity and Specificity of Using a Computer Aided Diagnosis Program for Automatically Scoring Chest X-Rays of Presumptive TB Patients Compared with Xpert® MTB/RIF in Lusaka Zambia.
Detection of Chest X-ray abnormalities and tuberculosis using computer-aided detection vs interpretation by radiologists and a clinical officer.
Computer-aided diagnosis of X-rays in a screening for pulmonary tuberculosis of a prison population in Tanzania.
Symptomatic screening and computer-aided radiography for active-case finding of tuberculosis: a prediction model for TB case detection.
Detection of tuberculosis with digital chest radiography: automatic reading versus interpretation by clinical officers.
Performance of inexperienced and experienced observers in detection of active tuberculosis on digital chest radiographs with and without the use of computer-aided diagnosis.
Computer-aided detection of tuberculosis among high risk groups: potential for automated triage.
Fast and effective quantification of symmetry in medical images for pathology detection: application to chest radiography.
Automatic detection of pleural effusion in chest radiographs.
On Combining Multiple-Instance Learning and Active Learning for Computer-Aided Detection of Tuberculosis.
Localized energy-based normalization of medical images: application to chest radiography.
Automatic detection of tuberculosis in chest radiographs using a combination of textural, focal, and shape abnormality analysis.
A Novel Multiple-Instance Learning-Based Approach to Computer-Aided Detection of Tuberculosis on Chest X-Rays.
Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming.
Multiple-instance learning for computer-aided detection of tuberculosis.
Suppression of translucent elongated structures: applications in chest radiography.
Foreign object detection and removal to improve automated analysis of chest radiographs.
Automated Scoring of Chest Radiographs for Tuberculosis Prevalence Surveys: A Combined Approach.
Automated localization of costophrenic recesses and costophrenic angle measurement on frontal chest radiographs.
Improved texture analysis for automatic detection of Tuberculosis (TB) on Chest Radiographs with Bone Suppression images.
Clavicle segmentation in chest radiographs.
Fusion of local and global detection systems to detect tuberculosis in chest radiographs.
Rib suppression in chest radiographs to improve classification of textural abnormalities.
Dissimilarity-based classification in the absence of local ground truth: application to the diagnostic interpretation of chest radiographs.
Computer-aided detection of interstitial abnormalities in chest radiographs using a reference standard based on computed tomography.
Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database.
Automatic detection of abnormalities in chest radiographs using local texture analysis.