Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Garnavi, Rahil"'
Autor:
Sedai, Suman, Antony, Bhavna, Rai, Ravneet, Jones, Katie, Ishikawa, Hiroshi, Schuman, Joel, Gadi, Wollstein, Garnavi, Rahil
Publikováno v:
MICCAI 2019 pp 282-290
Deep convolutional neural networks have shown outstanding performance in medical image segmentation tasks. The usual problem when training supervised deep learning methods is the lack of labeled data which is time-consuming and costly to obtain. In t
Externí odkaz:
http://arxiv.org/abs/2103.02083
Age-related macular degeneration (AMD) is one of the leading causes of permanent vision loss in people aged over 60 years. Accurate segmentation of biomarkers such as drusen that points to the early stages of AMD is crucial in preventing further visi
Externí odkaz:
http://arxiv.org/abs/2101.08888
Autor:
George, Yasmeen M., Sedai, Suman, Antony, Bhavna J., Ishikawa, Hiroshi, Wollstein, Gadi, Schuman, Joel S., Garnavi, Rahil
In optical coherence tomography (OCT) volumes of retina, the sequential acquisition of the individual slices makes this modality prone to motion artifacts, misalignments between adjacent slices being the most noticeable. Any distortion in OCT volumes
Externí odkaz:
http://arxiv.org/abs/2007.01522
Autor:
Maetschke, Stefan, Antony, Bhavna, Ishikawa, Hiroshi, Wollstein, Gadi, Schuman, Joel, Garnavi, Rahil
Visual field tests (VFT) are pivotal for glaucoma diagnosis and conducted regularly to monitor disease progression. Here we address the question to what degree aggregate VFT measurements such as Visual Field Index (VFI) and Mean Deviation (MD) can be
Externí odkaz:
http://arxiv.org/abs/1908.01428
Tree-like structures, such as blood vessels, often express complexity at very fine scales, requiring high-resolution grids to adequately describe their shape. Such sparse morphology can alternately be represented by locations of centreline points, bu
Externí odkaz:
http://arxiv.org/abs/1811.03208
Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our
Externí odkaz:
http://arxiv.org/abs/1809.04282
Localization of chest pathologies in chest X-ray images is a challenging task because of their varying sizes and appearances. We propose a novel weakly supervised method to localize chest pathologies using class aware deep multiscale feature learning
Externí odkaz:
http://arxiv.org/abs/1808.08280
The widely used ChestX-ray14 dataset addresses an important medical image classification problem and has the following caveats: 1) many lung pathologies are visually similar, 2) a variant of diseases including lung cancer, tuberculosis, and pneumonia
Externí odkaz:
http://arxiv.org/abs/1807.07247
Autor:
Maetschke, Stefan, Antony, Bhavna, Ishikawa, Hiroshi, Wollstein, Gadi, Schuman, Joel S., Garnavi, Rahil
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. Pr
Externí odkaz:
http://arxiv.org/abs/1807.04855
We present a conceptually new and flexible method for multi-class open set classification. Unlike previous methods where unknown classes are inferred with respect to the feature or decision distance to the known classes, our approach is able to provi
Externí odkaz:
http://arxiv.org/abs/1707.07418