Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-Task Learning

The need for comprehensive and automated screening methods for retinal image classification has long been recognized. Well-qualified doctors annotated images are very expensive and only a limited amount of data is available for various retinal diseases such as diabetic retinopathy (DR) and age-relat...

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Bibliographic Details
Main Authors:	Bonnington, P. (Author), Ge, Z. (Author), Ju, L. (Author), Lu, H. (Author), Mahapatra, D. (Author), Wang, X. (Author), Zhao, X. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	accuracy Additional experiments Age-related macular degeneration algorithm Algorithms Article artificial neural network Automated screening Automation Biomedical signal processing Classification algorithm computer model controlled study convolutional neural network Deep convolutional neural networks diabetic retinopathy Diabetic retinopathy Diabetic Retinopathy Diagnosis diagnostic imaging diagnostic test accuracy study distillation Distillation Distilleries entropy eye fundus Eye protection Fundus Oculi Grading human Humans Image classification Image enhancement Imaging applications Interpretability kernel method knowledge distillation learning algorithm Learning systems machine learning Medical imaging medical imaging classification Monitoring signals multiclass classification multi-task learning Multi-task learning Ophthalmology optical coherence tomography proliferative diabetic retinopathy refraction error reliability reproducibility Reproducibility of Results retina blood vessel retina disease retina image Retinal Diseases retinal pigment epithelium Semantics support vector machine time series analysis training
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Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-Task Learning

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