Rotation-oriented collaborative self-supervised learning for retinal disease diagnosis
The automatic diagnosis of various conventional ophthalmic diseases from fundus images is
important in clinical practice. However, developing such automatic solutions is challenging …
important in clinical practice. However, developing such automatic solutions is challenging …
Self-supervised feature learning via exploiting multi-modal data for retinal disease diagnosis
The automatic diagnosis of various retinal diseases from fundus images is important to
support clinical decision-making. However, developing such automatic solutions is …
support clinical decision-making. However, developing such automatic solutions is …
Application of semi-supervised learning in image classification: Research on fusion of labeled and unlabeled data
Deep learning has attracted wide attention recently because of its excellent feature
representation ability and end-to-end automatic learning method. Especially in clinical …
representation ability and end-to-end automatic learning method. Especially in clinical …
A foundation model for generalizable disease detection from retinal images
Medical artificial intelligence (AI) offers great potential for recognizing signs of health
conditions in retinal images and expediting the diagnosis of eye diseases and systemic …
conditions in retinal images and expediting the diagnosis of eye diseases and systemic …
An interpretable transformer network for the retinal disease classification using optical coherence tomography
J He, J Wang, Z Han, J Ma, C Wang, M Qi - Scientific Reports, 2023 - nature.com
Retinal illnesses such as age-related macular degeneration and diabetic macular edema
will lead to irreversible blindness. With optical coherence tomography (OCT), doctors are …
will lead to irreversible blindness. With optical coherence tomography (OCT), doctors are …
Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database
Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease
detection by using computer-aided diagnosis from fundus image has emerged as a new …
detection by using computer-aided diagnosis from fundus image has emerged as a new …
Robust collaborative learning of patch-level and image-level annotations for diabetic retinopathy grading from fundus image
Diabetic retinopathy (DR) grading from fundus images has attracted increasing interest in
both academic and industrial communities. Most convolutional neural network-based …
both academic and industrial communities. Most convolutional neural network-based …
EyeDeep-Net: a multi-class diagnosis of retinal diseases using deep neural network
Retinal images are a key element for ophthalmologists in diagnosing a variety of eye
illnesses. The retina is vulnerable to microvascular changes as a result of many retinal …
illnesses. The retina is vulnerable to microvascular changes as a result of many retinal …
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 …
AI for medical imaging goes deep
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