Historical and clinical contributions of the JSRT chest radiograph database to medical imaging research

Fujita H. AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiol Phys Technol. 2020;13:6–19.

Article  PubMed  Google Scholar 

Doi K. Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol. 2005;78:S3–19.

Article  PubMed  Google Scholar 

Shiraishi J, Katsuragawa S, Ikezoe J, Matsumoto T, Kobayashi T, Komatsu K, et al. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. AJR Am J Roentgenol. 2000;174:71–4.

Article  CAS  PubMed  Google Scholar 

Pesce LL, Metz CE. Reliable and computationally efficient maximum-likelihood estimation of “proper” binormal ROC curves. Acad Radiol. 2007;14:814–29.

Article  PubMed  PubMed Central  Google Scholar 

Li Q, Katsuragawa S, Ishida T, Yoshida H, Tsukuda S, MacMahon H, et al. Contralateral subtraction: a novel technique for detection of asymmetric abnormalities on digital chest radiographs. Med Phys. 2000;27:47–55.

Article  CAS  PubMed  Google Scholar 

Li Q, Katsuragawa S, Doi K. Computer-aided diagnostic scheme for lung nodule detection in digital chest radiographs by use of a multiple-template matching technique. Med Phys. 2001;28:2070–6.

Article  CAS  PubMed  Google Scholar 

Shiraishi J, Li Q, Suzuki K, Engelmann R, Doi K. Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification. Med Phys. 2006;33:2642–53.

Article  PubMed  Google Scholar 

Suzuki K, Shiraishi J, Abe H, MacMahon H, Doi K. False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of a massive training artificial neural network. Acad Radiol. 2005;12:191–201.

Article  PubMed  Google Scholar 

Shiraishi J, Abe H, Engelmann R, Aoyama M, MacMahon H, Doi K. Computer-aided diagnosis for distinction between benign and malignant solitary pulmonary nodules in chest radiographs: ROC analysis of radiologists’ performance. Radiology. 2003;227:469–74.

Article  PubMed  Google Scholar 

Schilham AMR, van Ginneken B, Loog M. A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database. Med Image Anal. 2006;10:247–58.

Article  PubMed  Google Scholar 

Campadelli P, Casiraghi E, Artioli A. A fully automated method for lung nodule detection from postero-anterior chest radiographs. IEEE Trans Med Imaging. 2006;25:1588–603.

Article  PubMed  Google Scholar 

Kakeda S, Moriya J, Sato H, Aoki T, Watanabe H, Nakata H, et al. Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system. AJR Am J Roentgenol. 2004;182:505–10.

Article  PubMed  Google Scholar 

Wang C, Elazab A, Wu J, Hu Q. Lung nodule classification using deep feature fusion in chest radiography. Comput Med Imaging Graph. 2017;57:10–8.

Article  PubMed  Google Scholar 

van Ginneken B, Stegmann MB, Loog M. Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Med Image Anal. 2006;10:19–40.

Article  PubMed  Google Scholar 

van Ginneken B. Segmentation masks for the JSRT chest radiograph database. Zenodo. https://doi.org/10.5281/zenodo.7056076

Novikov AA, Lenis D, Major D, Hladuvka J, Wimmer M, Buhler K. Fully convolutional architectures for multiclass segmentation in chest radiographs. IEEE Trans Med Imaging. 2018;37(8):1865–76.

Article  PubMed  Google Scholar 

Candemir S, Jaeger S, Palaniappan K, Musco JP, Singh RK, Xue Z, et al. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging. 2014;33:577–90.

Article  PubMed  Google Scholar 

Oh Y, Park S, Ye JC. Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans Med Imaging. 2020;39(8):2688–700.

Article  PubMed  Google Scholar 

Suzuki K, Abe H, MacMahon H, Doi K. Image-processing technique for suppressing ribs in chest radiographs by means of a massive training artificial neural network (MTANN). IEEE Trans Med Imaging. 2006;25:406–16.

Article  PubMed  Google Scholar 

Yang W, Chen Y, Liu Y, Zhong L, Qin G, Lu Z, et al. Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain. Med Image Anal. 2017;35:421–33.

Article  PubMed  Google Scholar 

Zamzmi G, Rajaraman S, Antani S. Accelerating super-resolution and visual task analysis in medical images. Appl Sci. 2020;10:4282.

Article  CAS  Google Scholar 

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