652 research outputs found
Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks
We propose an automatic diabetic retinopathy (DR) analysis algorithm based on
two-stages deep convolutional neural networks (DCNN). Compared to existing
DCNN-based DR detection methods, the proposed algorithm have the following
advantages: (1) Our method can point out the location and type of lesions in
the fundus images, as well as giving the severity grades of DR. Moreover, since
retina lesions and DR severity appear with different scales in fundus images,
the integration of both local and global networks learn more complete and
specific features for DR analysis. (2) By introducing imbalanced weighting map,
more attentions will be given to lesion patches for DR grading, which
significantly improve the performance of the proposed algorithm. In this study,
we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus
images from Kaggle competition dataset. Under the guidance of clinical
ophthalmologists, the experimental results show that our local lesion detection
net achieve comparable performance with trained human observers, and the
proposed imbalanced weighted scheme also be proved to significantly improve the
capability of our DCNN-based DR grading algorithm
Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images
Convolutional neural networks (CNNs) show impressive performance for image
classification and detection, extending heavily to the medical image domain.
Nevertheless, medical experts are sceptical in these predictions as the
nonlinear multilayer structure resulting in a classification outcome is not
directly graspable. Recently, approaches have been shown which help the user to
understand the discriminative regions within an image which are decisive for
the CNN to conclude to a certain class. Although these approaches could help to
build trust in the CNNs predictions, they are only slightly shown to work with
medical image data which often poses a challenge as the decision for a class
relies on different lesion areas scattered around the entire image. Using the
DiaretDB1 dataset, we show that on retina images different lesion areas
fundamental for diabetic retinopathy are detected on an image level with high
accuracy, comparable or exceeding supervised methods. On lesion level, we
achieve few false positives with high sensitivity, though, the network is
solely trained on image-level labels which do not include information about
existing lesions. Classifying between diseased and healthy images, we achieve
an AUC of 0.954 on the DiaretDB1.Comment: Accepted in Proc. IEEE International Conference on Image Processing
(ICIP), 201
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A Smartphone-Based Tool for Rapid, Portable, and Automated Wide-Field Retinal Imaging.
Purpose:High-quality, wide-field retinal imaging is a valuable method for screening preventable, vision-threatening diseases of the retina. Smartphone-based retinal cameras hold promise for increasing access to retinal imaging, but variable image quality and restricted field of view can limit their utility. We developed and clinically tested a smartphone-based system that addresses these challenges with automation-assisted imaging. Methods:The system was designed to improve smartphone retinal imaging by combining automated fixation guidance, photomontage, and multicolored illumination with optimized optics, user-tested ergonomics, and touch-screen interface. System performance was evaluated from images of ophthalmic patients taken by nonophthalmic personnel. Two masked ophthalmologists evaluated images for abnormalities and disease severity. Results:The system automatically generated 100° retinal photomontages from five overlapping images in under 1 minute at full resolution (52.3 pixels per retinal degree) fully on-phone, revealing numerous retinal abnormalities. Feasibility of the system for diabetic retinopathy (DR) screening using the retinal photomontages was performed in 71 diabetics by masked graders. DR grade matched perfectly with dilated clinical examination in 55.1% of eyes and within 1 severity level for 85.2% of eyes. For referral-warranted DR, average sensitivity was 93.3% and specificity 56.8%. Conclusions:Automation-assisted imaging produced high-quality, wide-field retinal images that demonstrate the potential of smartphone-based retinal cameras to be used for retinal disease screening. Translational Relevance:Enhancement of smartphone-based retinal imaging through automation and software intelligence holds great promise for increasing the accessibility of retinal screening
Automatic Classification of Bright Retinal Lesions via Deep Network Features
The diabetic retinopathy is timely diagonalized through color eye fundus
images by experienced ophthalmologists, in order to recognize potential retinal
features and identify early-blindness cases. In this paper, it is proposed to
extract deep features from the last fully-connected layer of, four different,
pre-trained convolutional neural networks. These features are then feeded into
a non-linear classifier to discriminate three-class diabetic cases, i.e.,
normal, exudates, and drusen. Averaged across 1113 color retinal images
collected from six publicly available annotated datasets, the deep features
approach perform better than the classical bag-of-words approach. The proposed
approaches have an average accuracy between 91.23% and 92.00% with more than
13% improvement over the traditional state of art methods.Comment: Preprint submitted to Journal of Medical Imaging | SPIE (Tue, Jul 28,
2017
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