activity
20182021
most citedA feature agnostic approach for glaucoma detection in OCT volumes

215 citations · 221 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2021

Uncertainty guided semi-supervised segmentation of retinal layers in OCT images

Suman Sedai, Bhavna Antony, Ravneet Rai +5

Deep convolutional neural networks have shown outstanding performance in medical image segmentation tasks. The usual problem when training supervised deep learning methods is the l…

cs.LG2020

Dueling Deep Q-Network for Unsupervised Inter-frame Eye Movement Correction in Optical Coherence Tomography Volumes

Yasmeen M. George, Suman Sedai, Bhavna J. Antony +4

In optical coherence tomography (OCT) volumes of retina, the sequential acquisition of the individual slices makes this modality prone to motion artifacts, misalignments between ad…

cs.CV2019★ 2 cited

Inference of visual field test performance from OCT volumes using deep learning

Stefan Maetschke, Bhavna Antony, Hiroshi Ishikawa +3

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 measur…

cs.CV2018★ 4 cited

Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning

Suman Sedai, Bhavna Antony, Dwarikanath Mahapatra +1

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 a…

cs.CV2018★ 215 cited

A feature agnostic approach for glaucoma detection in OCT volumes

Stefan Maetschke, Bhavna Antony, Hiroshi Ishikawa +3

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 (GCI…