[HTML][HTML] Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey
Background Recent advances in deep learning techniques have led to improved diagnostic
abilities in ophthalmology. A generative adversarial network (GAN), which consists of two …
abilities in ophthalmology. A generative adversarial network (GAN), which consists of two …
[HTML][HTML] A deep learning method for automatic segmentation of the bony orbit in MRI and CT images
This paper proposes a fully automatic method to segment the inner boundary of the bony
orbit in two different image modalities: magnetic resonance imaging (MRI) and computed …
orbit in two different image modalities: magnetic resonance imaging (MRI) and computed …
Segmentation of ultrasound image sequences by combing a novel deep siamese network with a deformable contour model
Deformable contours are widely applied in medical image segmentation, which are usually
derived from appearance cues in medical images. However, the performance of deformed …
derived from appearance cues in medical images. However, the performance of deformed …
[HTML][HTML] Generative artificial intelligence in ophthalmology
Generative AI has revolutionized medicine over the past several years. A generative
adversarial network (GAN) is a deep learning framework that has become a powerful …
adversarial network (GAN) is a deep learning framework that has become a powerful …
[HTML][HTML] A two-stage GAN for high-resolution retinal image generation and segmentation
In this paper, we use Generative Adversarial Networks (GANs) to synthesize high-quality
retinal images along with the corresponding semantic label-maps, instead of real images …
retinal images along with the corresponding semantic label-maps, instead of real images …
[HTML][HTML] Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning
Detecting clinical keratoconus (KCN) poses a challenging and time-consuming task. During
the diagnostic process, ophthalmologists are required to review demographic and clinical …
the diagnostic process, ophthalmologists are required to review demographic and clinical …
[HTML][HTML] Enhanced OCT chorio-retinal segmentation in low-data settings with semi-supervised GAN augmentation using cross-localisation
Training deep learning methods for optical coherence tomography (OCT) retinal and
choroidal layer segmentation is a challenge when data is scarce. In medical image analysis …
choroidal layer segmentation is a challenge when data is scarce. In medical image analysis …
[HTML][HTML] Image-to-image translation with generative adversarial networks via retinal masks for realistic optical coherence tomography imaging of diabetic macular …
One of the main issues with deep learning is the need of a significant number of samples.
We intend to address this problem in the field of Optical Coherence Tomography (OCT) …
We intend to address this problem in the field of Optical Coherence Tomography (OCT) …
[HTML][HTML] Synthetic OCT data in challenging conditions: three-dimensional OCT and presence of abnormalities
H Danesh, K Maghooli, A Dehghani… - Medical & Biological …, 2022 - Springer
Nowadays, retinal optical coherence tomography (OCT) plays an important role in
ophthalmology and automatic analysis of the OCT is of real importance: image denoising …
ophthalmology and automatic analysis of the OCT is of real importance: image denoising …
[HTML][HTML] A review of generative adversarial network applications in optical coherence tomography image analysis
Optical coherence tomography (OCT) has revolutionized ophthalmic clinical practice and
research, as a result of the high-resolution images that the method is able to capture in a …
research, as a result of the high-resolution images that the method is able to capture in a …