NDLI: Microaneurysms segmentation and diabetic retinopathy detection by learning discriminative representations

Content Provider	IET Digital Library
Author	Sarhan, Mhd Hasan Albarqouni, Shadi Yigitsoy, Mehmet Navab, Nassir Abouzar, Eslami
Abstract	Deep learning techniques are recently being used in fundus image analysis and diabetic retinopathy detection. Microaneurysms are important indicators of diabetic retinopathy progression. The authors introduce a two-stage deep learning approach for microaneurysms segmentation using multiple scales of the input with selective sampling and embedding triplet loss. The proposed approach facilitates a region proposal fully convolutional neural network trained on segmented patches and a patch-wise refinement network for improving the results suggested by the first stage hypothesis. To enhance the discriminative power of the second stage refinement network, the authors use triplet embedding loss with a selective sampling routine that dynamically assigns sampling probabilities to the oversampled class patches. This approach introduces a 23.5 % relative improvement over the vanilla fully convolutional neural network on the Indian Diabetic Retinopathy Image Data set segmentation data set. The proposed segmentation is incorporated in a classification model to solve two downstream tasks for diabetic retinopathy detection and referable diabetic retinopathy detection. The classification tasks are trained on the Kaggle diabetic retinopathy challenge data set and evaluated on the Messidor data. The authors show that adding the segmentation enhances the classification performance and achieves comparable performance to the state-of-the-art models.
Starting Page	4571
Ending Page	4578
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 17, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0804
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/17
Journal	IET Image Processing
Publisher Date	2021-01-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Measurement And Imaging Computer Vision And Image Processing Technique Deep Learning Approach Deep Learning Technique Diabetic Retinopathy Progression Diseases Embedding Triplet Loss Eye Fundus Image Analysis Health Physics Image Classification Image Recognition Image Representation Image Sampling Image Segmentation Indian Diabetic Retinopathy Image Data Kaggle Diabetic Retinopathy Challenge Data Learning in AI Medical And Biomedical Uses of Field Medical Image Processing Microaneurysms Segmentation Neural Nets Patch-wise Refinement Network Patient Diagnostic Method And Instrumentation Radiations Radioactivity Referable Diabetic Retinopathy Detection Segmentation Data Segmented Patches Selective Sampling Routine Stage Refinement Network Triplet Embedding Loss Vanilla Fully Convolutional Neural Network
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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