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Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-Ray Segmentation

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Author(s): Cheng Chen , Qi Dou , Hao Chen , Pheng-Ann Heng

Publication date (Online): September 15 2018

Publisher: Springer International Publishing

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Adversarial Discriminative Domain Adaptation

Eric Tzeng, Judy Hoffman, Kate Saenko … (2017)

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Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

Konstantinos Bousmalis, Nathan Silberman, David Dohan … (2017)

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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.

J. Shiraishi, S Katsuragawa, J Ikezoe … (2000)

We developed a digital image database (www.macnet.or.jp/jsrt2/cdrom_nodules.html ) of 247 chest radiographs with and without a lung nodule. The aim of this study was to investigate the characteristics of image databases for potential use in various digital image research projects. Radiologists' detection of solitary pulmonary nodules included in the database was evaluated using a receiver operating characteristic (ROC) analysis.

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Publication date (Print): 2018

Publication date (Online): September 15 2018

Pages: 143-151

DOI: 10.1007/978-3-030-00919-9_17

SO-VID: 92e2bace-115e-4bff-8119-9a2b9a25b7cf

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Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-Ray Segmentation

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Most cited references 11

Adversarial Discriminative Domain Adaptation

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

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.

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