Diabetic Retinopathy Diagnosis Using Multichannel Generative Adversarial Network With Semisupervision

Diabetic retinopathy (DR) is one of the major causes of blindness. It is of great significance to apply deep-learning techniques for DR recognition. However, deep-learning algorithms often depend on large amounts of labeled data, which is expensive and time-consuming to obtain in the medical imaging...

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Veröffentlicht in:	IEEE transactions on automation science and engineering 2021-04, Vol.18 (2), p.574-585
Hauptverfasser:	Wang, Shuqiang, Wang, Xiangyu, Hu, Yong, Shen, Yanyan, Yang, Zhile, Gan, Min, Lei, Baiying
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Annotations Biomedical imaging Blindness Computer-aided diagnosis (CAD) Data analysis Data models Diabetes Diabetic retinopathy diabetic retinopathy (DR) Diagnosis Feature extraction Gallium nitride generative adversarial network (GAN) Generative adversarial networks High resolution Image analysis Image compression Image resolution Lesions Machine learning Medical imaging Model accuracy multichannel Recognition Scattering semisupervised learning Solid modeling
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