24 citations · 33 across the 11 of their papers we have counts for
7 papers
Retinal OCT Denoising with Pseudo-Multimodal Fusion Network
Dewei Hu, Joseph D. Malone, Yigit Atay +2
Optical coherence tomography (OCT) is a prevalent imaging technique for retina. However, it is affected by multiplicative speckle noise that can degrade the visibility of essential…
LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation
Dewei Hu, Can Cui, Hao Li +3
Optical coherence tomography (OCT) is a non-invasive imaging technique widely used for ophthalmology. It can be extended to OCT angiography (OCT-A), which reveals the retinal vascu…
Multiple Sclerosis Lesion Segmentation -- A Survey of Supervised CNN-Based Methods
Huahong Zhang, Ipek Oguz
Lesion segmentation is a core task for quantitative analysis of MRI scans of Multiple Sclerosis patients. The recent success of deep learning techniques in a variety of medical ima…
Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis of Deformation Fields in Huntington's Disease
Kilian Hett, Hans Johnson, Pierrick Coupé +3
The improvements in magnetic resonance imaging have led to the development of numerous techniques to better detect structural alterations caused by neurodegenerative diseases. Amon…
Medical Imaging with Deep Learning: MIDL 2019 -- Extended Abstract Track
M. Jorge Cardoso, Aasa Feragen, Ben Glocker +4
This compendium gathers all the accepted extended abstracts from the Second International Conference on Medical Imaging with Deep Learning (MIDL 2019), held in London, UK, 8-10 Jul…
Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative
Satyananda Kashyap, Ipek Oguz, Honghai Zhang +1
We present a fully automated learning-based approach for segmenting knee cartilage in the presence of osteoarthritis (OA). The algorithm employs a hierarchical set of two random fo…