346 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
Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.
Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Leveraging Disease Progression Learning for Medical Image Recognition
Unlike natural images, medical images often have intrinsic characteristics
that can be leveraged for neural network learning. For example, images that
belong to different stages of a disease may continuously follow a certain
progression pattern. In this paper, we propose a novel method that leverages
disease progression learning for medical image recognition. In our method,
sequences of images ordered by disease stages are learned by a neural network
that consists of a shared vision model for feature extraction and a long
short-term memory network for the learning of stage sequences. Auxiliary vision
outputs are also included to capture stage features that tend to be discrete
along the disease progression. Our proposed method is evaluated on a public
diabetic retinopathy dataset, and achieves about 3.3% improvement in disease
staging accuracy, compared to the baseline method that does not use disease
progression learning
Supervised machine learning based multi-task artificial intelligence classification of retinopathies
Artificial intelligence (AI) classification holds promise as a novel and
affordable screening tool for clinical management of ocular diseases. Rural and
underserved areas, which suffer from lack of access to experienced
ophthalmologists may particularly benefit from this technology. Quantitative
optical coherence tomography angiography (OCTA) imaging provides excellent
capability to identify subtle vascular distortions, which are useful for
classifying retinovascular diseases. However, application of AI for
differentiation and classification of multiple eye diseases is not yet
established. In this study, we demonstrate supervised machine learning based
multi-task OCTA classification. We sought 1) to differentiate normal from
diseased ocular conditions, 2) to differentiate different ocular disease
conditions from each other, and 3) to stage the severity of each ocular
condition. Quantitative OCTA features, including blood vessel tortuosity (BVT),
blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel
density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour
irregularity (FAZ-CI) were fully automatically extracted from the OCTA images.
A stepwise backward elimination approach was employed to identify sensitive
OCTA features and optimal-feature-combinations for the multi-task
classification. For proof-of-concept demonstration, diabetic retinopathy (DR)
and sickle cell retinopathy (SCR) were used to validate the supervised machine
leaning classifier. The presented AI classification methodology is applicable
and can be readily extended to other ocular diseases, holding promise to enable
a mass-screening platform for clinical deployment and telemedicine.Comment: Supplemental material attached at the en
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