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Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs., , , and . EMBC, page 3689-3692. IEEE, (2019)Annotations of Lung Abnormalities in the Shenzhen Chest X-ray Dataset for Computer-Aided Screening of Pulmonary Diseases., , , , , , , , and . Data, 7 (7): 95 (2022)Improved TB classification using bone-suppressed chest radiographs., , , , and . CoRR, (2021)Deep ensemble learning for segmenting tuberculosis-consistent manifestations in chest radiographs., , , , , and . CoRR, (2022)Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?, , , , and . CoRR, (2023)Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays., , , , , and . IEEE Access, (2020)Modality-Specific Deep Learning Model Ensembles Toward Improving TB Detection in Chest Radiographs., and . IEEE Access, (2020)Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs., , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10950 of SPIE Proceedings, page 109500S. SPIE, (2019)Can deep adult lung segmentation models generalize to the pediatric population?, , , , and . Expert Syst. Appl., 229 (Part A): 120531 (November 2023)A bone suppression model ensemble to improve COVID-19 detection in chest X-rays., , , and . CoRR, (2021)