208 research outputs found
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
Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection
We propose a convolution neural network based algorithm for simultaneously
diagnosing diabetic retinopathy and highlighting suspicious regions. Our
contributions are two folds: 1) a network termed Zoom-in-Net which mimics the
zoom-in process of a clinician to examine the retinal images. Trained with only
image-level supervisions, Zoomin-Net can generate attention maps which
highlight suspicious regions, and predicts the disease level accurately based
on both the whole image and its high resolution suspicious patches. 2) Only
four bounding boxes generated from the automatically learned attention maps are
enough to cover 80% of the lesions labeled by an experienced ophthalmologist,
which shows good localization ability of the attention maps. By clustering
features at high response locations on the attention maps, we discover
meaningful clusters which contain potential lesions in diabetic retinopathy.
Experiments show that our algorithm outperform the state-of-the-art methods on
two datasets, EyePACS and Messidor.Comment: accepted by MICCAI 201
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