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Found 10 papers, 3 papers with code
Uncertainty guided semi-supervised segmentation of retinal layers in OCT images
First, a teacher segmentation model is trained from the labeled samples using Bayesian deep learning.
Analyzing Epistemic and Aleatoric Uncertainty for Drusen Segmentation in Optical Coherence Tomography Images
Age-related macular degeneration (AMD) is one of the leading causes of permanent vision loss in people aged over 60 years.
Dueling Deep Q-Network for Unsupervised Inter-frame Eye Movement Correction in Optical Coherence Tomography Volumes
We also compare our model with elastix intensity based medical image registration approach, where significant improvement is achieved by our model for both noisy and denoised volumes.
Inference of visual field test performance from OCT volumes using deep learning
Visual field tests (VFT) are pivotal for glaucoma diagnosis and conducted regularly to monitor disease progression.
Deep Semantic Instance Segmentation of Tree-like Structures Using Synthetic Data
Tree-like structures, such as blood vessels, often express complexity at very fine scales, requiring high-resolution grids to adequately describe their shape.
Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning
Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases.
Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images
We propose a novel weakly supervised method to localize chest pathologies using class aware deep multiscale feature learning.
Chest X-rays Classification: A Multi-Label and Fine-Grained Problem
In this work we have proposed a novel error function, Multi-label Softmax Loss (MSML), to specifically address the properties of multiple labels and imbalanced data.
A feature agnostic approach for glaucoma detection in OCT volumes
Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly used for the diagnosis and monitoring of glaucoma.
Generative OpenMax for Multi-Class Open Set Classification
We present a conceptually new and flexible method for multi-class open set classification.
