55 citations · 67 across the 5 of their papers we have counts for
8 papers · 1 filter
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…
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…
Deep Semantic Instance Segmentation of Tree-like Structures Using Synthetic Data
Kerry Halupka, Rahil Garnavi, Stephen Moore
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 morpholog…
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…
Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images
Suman Sedai, Dwarikanath Mahapatra, Zongyuan Ge +2
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 localiz…
Chest X-rays Classification: A Multi-Label and Fine-Grained Problem
Zongyuan Ge, Dwarikanath Mahapatra, Suman Sedai +2
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…