Ophthalmic diagnosis using deep learning with fundus images–A critical review
An overview of the applications of deep learning for ophthalmic diagnosis using retinal
fundus images is presented. We describe various retinal image datasets that can be used for …
fundus images is presented. We describe various retinal image datasets that can be used for …
Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Glaucoma is one of the leading causes of irreversible but preventable blindness in working
age populations. Color fundus photography (CFP) is the most cost-effective imaging …
age populations. Color fundus photography (CFP) is the most cost-effective imaging …
Idrid: Diabetic retinopathy–segmentation and grading challenge
Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss,
predominantly affecting the working-age population across the globe. Screening for DR …
predominantly affecting the working-age population across the globe. Screening for DR …
[HTML][HTML] Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images
Purpose To develop and evaluate deep learning models that screen multiple abnormal
findings in retinal fundus images. Design Cross-sectional study. Participants For the …
findings in retinal fundus images. Design Cross-sectional study. Participants For the …
Guided soft attention network for classification of breast cancer histopathology images
H Yang, JY Kim, H Kim… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
An attention guided convolutional neural network (CNN) for the classification of breast
cancer histopathology images is proposed. Neural networks are generally applied as black …
cancer histopathology images is proposed. Neural networks are generally applied as black …
Rim-one dl: A unified retinal image database for assessing glaucoma using deep learning
The first version of the Retinal IMage database for Optic Nerve Evaluation (RIM-ONE) was
published in 2011. This was followed by two more, turning it into one of the most cited public …
published in 2011. This was followed by two more, turning it into one of the most cited public …
Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis--A Review
An overview of the applications of deep learning in ophthalmic diagnosis using retinal
fundus images is presented. We also review various retinal image datasets that can be used …
fundus images is presented. We also review various retinal image datasets that can be used …
MPSA: Multi-Position Supervised Soft Attention-based convolutional neural network for histopathological image classification
B Qing, S Zhanquan, W Kang, W Chaoli… - Expert Systems with …, 2024 - Elsevier
In recent years, significant achievements have been made in the field of histopathological
image analysis using convolutional neural networks (CNNs). However, existing CNNs fail to …
image analysis using convolutional neural networks (CNNs). However, existing CNNs fail to …
Retinal glaucoma public datasets: what do we have and what is missing?
Public databases for glaucoma studies contain color images of the retina, emphasizing the
optic papilla. These databases are intended for research and standardized automated …
optic papilla. These databases are intended for research and standardized automated …
Risk identification of diabetic macular edema using e-adoption of emerging technology
The accumulation of the blood leaks on the retina is known as diabetic macular edema
(DME), which can result in irreversible blindness. Early diagnosis and therapy can stop …
(DME), which can result in irreversible blindness. Early diagnosis and therapy can stop …