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Literature DB >> 25525580

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

Stefan Jaeger¹, Sema Candemir¹, Sameer Antani¹, Yì-Xiáng J Wáng¹, Pu-Xuan Lu¹, George Thoma¹.

Abstract

The U.S. National Library of Medicine has made two datasets of postero-anterior (PA) chest radiographs available to foster research in computer-aided diagnosis of pulmonary diseases with a special focus on pulmonary tuberculosis (TB). The radiographs were acquired from the Department of Health and Human Services, Montgomery County, Maryland, USA and Shenzhen No. 3 People's Hospital in China. Both datasets contain normal and abnormal chest X-rays with manifestations of TB and include associated radiologist readings.

Entities: Disease Species

Keywords: Tuberculosis (TB); automatic screening; chest X-rays; computer-aided diagnosis; medical imaging

Year: 2014 PMID： 25525580 PMCID： PMC4256233 DOI： 10.3978/j.issn.2223-4292.2014.11.20

Source DB: PubMed Journal: Quant Imaging Med Surg ISSN： 2223-4306

3 in total

1. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration.

Authors: Sema Candemir; Stefan Jaeger; Kannappan Palaniappan; Jonathan P Musco; Rahul K Singh; Alexandros Karargyris; Sameer Antani; George Thoma; Clement J McDonald
Journal: IEEE Trans Med Imaging Date: 2013-11-13 Impact factor: 10.048

2. Automatic tuberculosis screening using chest radiographs.

Authors: Stefan Jaeger; Alexandros Karargyris; Sema Candemir; Les Folio; Jenifer Siegelman; Fiona Callaghan; Kannappan Palaniappan; Rahul K Singh; Sameer Antani; George Thoma; Clement J McDonald
Journal: IEEE Trans Med Imaging Date: 2013-10-01 Impact factor: 10.048

3. Automatic screening for tuberculosis in chest radiographs: a survey.

Authors: Stefan Jaeger; Alexandros Karargyris; Sema Candemir; Jenifer Siegelman; Les Folio; Sameer Antani; George Thoma
Journal: Quant Imaging Med Surg Date: 2013-04

3 in total

92 in total

1. 7^th Union Europe Conference on Lung Health, 22-24 June 2016, Bratislava (Slovakia): a delegate report.

Authors: Aliaksandr Skrahin
Journal: Quant Imaging Med Surg Date: 2016-08

2. Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs.

Authors: Tae Kyung Kim; Paul H Yi; Jinchi Wei; Ji Won Shin; Gregory Hager; Ferdinand K Hui; Haris I Sair; Cheng Ting Lin
Journal: J Digit Imaging Date: 2019-12 Impact factor: 4.056

3. Modality-specific deep learning model ensembles toward improving TB detection in chest radiographs.

Authors: Sivaramakrishnan Rajaraman; Sameer K Antani
Journal: IEEE Access Date: 2020-02-03 Impact factor: 3.367

4. Proposing a novel multi-instance learning model for tuberculosis recognition from chest X-ray images based on CNNs, complex networks and stacked ensemble.

Authors: Toktam Khatibi; Ali Shahsavari; Ali Farahani
Journal: Phys Eng Sci Med Date: 2021-02-22

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

1. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration.

2. Automatic tuberculosis screening using chest radiographs.

3. Automatic screening for tuberculosis in chest radiographs: a survey.

1. 7^th Union Europe Conference on Lung Health, 22-24 June 2016, Bratislava (Slovakia): a delegate report.

2. Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs.

3. Modality-specific deep learning model ensembles toward improving TB detection in chest radiographs.

4. Proposing a novel multi-instance learning model for tuberculosis recognition from chest X-ray images based on CNNs, complex networks and stacked ensemble.

5. COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays.

6. FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret.

7. Automated COVID-19 Detection from Chest X-Ray Images: A High-Resolution Network (HRNet) Approach.

8. NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification.

9. Detection and Semiquantitative Analysis of Cardiomegaly, Pneumothorax, and Pleural Effusion on Chest Radiographs.

10. Chest X-ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings.