1,382 research outputs found
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading
Diabetic retinopathy (DR) is a common retinal disease that leads to
blindness. For diagnosis purposes, DR image grading aims to provide automatic
DR grade classification, which is not addressed in conventional research
methods of binary DR image classification. Small objects in the eye images,
like lesions and microaneurysms, are essential to DR grading in medical
imaging, but they could easily be influenced by other objects. To address these
challenges, we propose a new deep learning architecture, called BiRA-Net, which
combines the attention model for feature extraction and bilinear model for
fine-grained classification. Furthermore, in considering the distance between
different grades of different DR categories, we propose a new loss function,
called grading loss, which leads to improved training convergence of the
proposed approach. Experimental results are provided to demonstrate the
superior performance of the proposed approach.Comment: Accepted at ICIP 201
Pathological Evidence Exploration in Deep Retinal Image Diagnosis
Though deep learning has shown successful performance in classifying the
label and severity stage of certain disease, most of them give few evidence on
how to make prediction. Here, we propose to exploit the interpretability of
deep learning application in medical diagnosis. Inspired by Koch's Postulates,
a well-known strategy in medical research to identify the property of pathogen,
we define a pathological descriptor that can be extracted from the activated
neurons of a diabetic retinopathy detector. To visualize the symptom and
feature encoded in this descriptor, we propose a GAN based method to synthesize
pathological retinal image given the descriptor and a binary vessel
segmentation. Besides, with this descriptor, we can arbitrarily manipulate the
position and quantity of lesions. As verified by a panel of 5 licensed
ophthalmologists, our synthesized images carry the symptoms that are directly
related to diabetic retinopathy diagnosis. The panel survey also shows that our
generated images is both qualitatively and quantitatively superior to existing
methods.Comment: to appear in AAAI (2019). The first two authors contributed equally
to the paper. Corresponding Author: Feng L
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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