https://scholars.lib.ntu.edu.tw/handle/123456789/611666
標題: | Task-GAN: Improving Generative Adversarial Network for Image Reconstruction | 作者: | Ouyang J. Wang G. Gong E. Chen K. Pauly J. Zaharchuk G. TZE-HSIANG CHEN |
關鍵字: | Computer aided instruction;Deep learning;Diagnosis;Image enhancement;Machine learning;Medical image processing;Restoration;Adversarial networks;Diagnostic features;GaN based;Generalized models;Medical data sets;Reconstruction frameworks;Reconstruction problems;Image reconstruction | 公開日期: | 2019 | 卷: | 11905 LNCS | 起(迄)頁: | 193-204 | 來源出版物: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 摘要: | Generative Adversarial Network (GAN) has demonstrated great potentials in computer vision tasks such as image restoration. However, image restoration for specific scenarios, such as medical image enhancement is still facing challenge: How to ensure the visually plausible results while not containing hallucinated features that might jeopardize downstream tasks such as pathology identification? Here, we propose Task-GAN, a generalized model for medical reconstruction problem. A task-specific network that captures the diagnostic/pathology features, was added to couple the GAN based image reconstruction framework. Validated on multiple medical datasets, we demonstrated that the proposed method leads to improved deep learning based image reconstruction while preserving the detailed structure and diagnostic features. ? Springer Nature Switzerland AG 2019. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076200773&doi=10.1007%2f978-3-030-33843-5_18&partnerID=40&md5=6460484e70b866c1205ae45a73a8b5f0 https://scholars.lib.ntu.edu.tw/handle/123456789/611666 |
DOI: | 10.1007/978-3-030-33843-5_18 |
顯示於: | 醫學工程學研究所 |
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