Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: A review
MB Gonçalves, LF Nakayama, D Ferraz, H Faber… - Eye, 2024 - nature.com
This study aimed to evaluate the image quality assessment (IQA) and quality criteria
employed in publicly available datasets for diabetic retinopathy (DR). A literature search …
employed in publicly available datasets for diabetic retinopathy (DR). A literature search …
A foundation language-image model of the retina (flair): Encoding expert knowledge in text supervision
Foundation vision-language models are currently transforming computer vision, and are on
the rise in medical imaging fueled by their very promising generalization capabilities …
the rise in medical imaging fueled by their very promising generalization capabilities …
Discriminative kernel convolution network for multi-label ophthalmic disease detection on imbalanced fundus image dataset
It is feasible to recognize the presence and seriousness of eye disease by investigating the
progressions in retinal biological structures. Fundus examination is a diagnostic procedure …
progressions in retinal biological structures. Fundus examination is a diagnostic procedure …
An open dataset for intelligent recognition and classification of abnormal condition in longwall mining
W Yang, X Zhang, B Ma, Y Wang, Y Wu, J Yan, Y Liu… - Scientific Data, 2023 - nature.com
The underground coal mine production of the fully mechanized mining face exists many
problems, such as poor operating environment, high accident rate and so on. Recently, the …
problems, such as poor operating environment, high accident rate and so on. Recently, the …
Lesion-based contrastive learning for diabetic retinopathy grading from fundus images
Manually annotating medical images is extremely expensive, especially for large-scale
datasets. Self-supervised contrastive learning has been explored to learn feature …
datasets. Self-supervised contrastive learning has been explored to learn feature …
Ssit: Saliency-guided self-supervised image transformer for diabetic retinopathy grading
Self-supervised Learning (SSL) has been widely applied to learn image representations
through exploiting unlabeled images. However, it has not been fully explored in the medical …
through exploiting unlabeled images. However, it has not been fully explored in the medical …
Open fundus photograph dataset with pathologic myopia recognition and anatomical structure annotation
Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly
myopic population. Early screening of this condition can reduce the damage caused by the …
myopic population. Early screening of this condition can reduce the damage caused by the …
YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation
Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data
annotation cost and model performance through employing sparsely-grained (ie, point-, box …
annotation cost and model performance through employing sparsely-grained (ie, point-, box …
I-secret: Importance-guided fundus image enhancement via semi-supervised contrastive constraining
Fundus image quality is crucial for screening various ophthalmic diseases. In this paper, we
proposed and validated a novel fundus image enhancement method, named importance …
proposed and validated a novel fundus image enhancement method, named importance …
Uni4Eye++: A General Masked Image Modeling Multi-modal Pre-training Framework for Ophthalmic Image Classification and Segmentation
A large-scale labeled dataset is a key factor for the success of supervised deep learning in
most ophthalmic image analysis scenarios. However, limited annotated data is very common …
most ophthalmic image analysis scenarios. However, limited annotated data is very common …