Automatic Diagnosis of Glaucoma on Color Fundus Images Using Adaptive Mask Deep Network

[1]  Jun Wu,et al.  Four Models for Automatic Recognition of Left and Right Eye in Fundus Images , 2018, MMM.

[2]  Xiaoxiao Li,et al.  REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs , 2019, Medical Image Anal..

[3]  Jianping Fan,et al.  AttenNet: Deep Attention Based Retinal Disease Classification in OCT Images , 2019, MMM.

[4]  Sang Jun Park,et al.  Classification of Findings with Localized Lesions in Fundoscopic Images Using a Regionally Guided CNN , 2018, COMPAY/OMIA@MICCAI.

[5]  Keerthi Ram,et al.  Joint Optic Disc and Cup Segmentation Using Fully Convolutional and Adversarial Networks , 2017, FIFI/OMIA@MICCAI.

[6]  Dwarikanath Mahapatra,et al.  Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images , 2018, MLMI@MICCAI.

[7]  Hongyan Liu,et al.  Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models , 2018, Knowl. Based Syst..

[8]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[9]  H. Quigley,et al.  The number of people with glaucoma worldwide in 2010 and 2020 , 2006, British Journal of Ophthalmology.

[10]  Xiaogang Wang,et al.  Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection , 2017, MICCAI.

[11]  Stuart Keel,et al.  Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma , 2019, JAMA ophthalmology.