Two public chest X-ray datasets for computer-aided screening of pulmonary diseases.

By Stefan Jaeger, Sema Candemir, Sameer Kiran Antani, Yì-Xiáng J Wáng, P. Lu, George R. Thoma. Quantitative Imaging in Medicine and Surgery, 2014

1,079 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0fd259546e31fa7ff4df38d21d18c149177faeb8

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Citations in the corpus

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets (2020, background): “National Library of Medicine (USNLM) collected Montgomery Country (MC) dataset [21].”
Segment Anything Model for medical image analysis: an experimental study (2023, background): “Xray-Chest Montgomery County and Shenzhen Chest X-ray Datasets [7] X-ray 1 Chest 704”
MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports (2019, background): “The Montgomery County chest X-ray set (MC) and the Shenzhen Hospital X-ray set (SH) are publicly available datasets focusing on classification of tuberculosis 29,30 .”
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks (2017, positive): “Detection of car-diomegaly is done only for the frontal CXR images from the Indiana Dataset.”
Towards Generalist Biomedical AI (2023, background): “The dataset contains 138 frontal chest X-rays, of which 80 are normal cases and 58 cases have manifestations of TB [59].”
Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification (2018, background): “The Shenzhen chest X-ray set [26] has a total of 662 images belonging to two categories (normal and tuberculosis (TB)).”
CFCM: Segmentation via Coarse to Fine Context Memory (2018, methodology): “This dataset comprises 138 annotated posterior-anterior chest x-rays and has been acquired from the tuberculosis con-trol program of the Department of Health and Human Services of Montgomery County, MD, USA [12].”
A generalist vision–language foundation model for diverse biomedical tasks (2023, positive): “…downstream tasks stemmed from their potential real-world applications: medical-image classification can Notably, on the SZ-CXR and MC-CXR datasets 38 (binary classification), BiomedGPT had accuracies of 97.0% and 89.7%, reflecting improvements of 6.0% and 0.8%, respectively, over the previously…”
Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models (2020, methodology): “We use 528 CXRs for training and 176 for validation without any data augmentation.”
MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models (2020, methodology): “In addition to ﬁne-tuning on labeled dataset from the source dataset, we also experiment with transfer learning to another small chest X-ray dataset with a different task (Jaeger et al., 2014).”