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- The University of MelbourneAU6 papers
- Monash UniversityAU5 papers
- NYU Langone HealthUS2 papers
- Australian College of OptometryAU1 paper
- Dalian University of TechnologyCN1 paper
- Data61AU1 paper
- Defence Science and Technology GroupAU1 paper
- Duke UniversityUS1 paper
- École Polytechnique Fédérale de LausanneCH1 paper
- Fuzhou UniversityCN1 paper
- Georgia Institute of TechnologyUS1 paper
- IBM (Brazil)BR1 paper
4 papers · 2 filters
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…
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…