65 Result(s)
within
English
Computer Science
Artificial Intelligence
Image Processing and Computer Vision
Pattern Recognition
M. **gliang
Conference Paper
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Chapter and Conference Paper
Over-and-Under Complete Convolutional RNN for MRI Reconstruction
Reconstructing magnetic resonance (MR) images from under-sampled data is a challenging problem due to various artifacts introduced by the under-sampling operation. Recent deep learning-based methods for MR ima...
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Chapter and Conference Paper
Lossless Image Compression Using a Multi-scale Progressive Statistical Model
Lossless image compression is an important technique for image storage and transmission when information loss is not allowed. With the fast development of deep learning techniques, deep neural networks have be...
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Chapter and Conference Paper
Lesion Mask-Based Simultaneous Synthesis of Anatomic and Molecular MR Images Using a GAN
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas for patients with malignant gliomas in neuro-oncology with the help of conventional an...
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Chapter and Conference Paper
Few Is Enough: Task-Augmented Active Meta-learning for Brain Cell Classification
Deep Neural Networks (or DNNs) must constantly cope with distribution changes in the input data when the task of interest or the data collection protocol changes. Retraining a network from scratch to combat th...
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Chapter and Conference Paper
MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net
Accurate hepatic vessel segmentation and registration using ultrasound (US) can contribute to beneficial navigation during hepatic surgery. However, it is challenging due to noise and speckle in US imaging and...
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Chapter and Conference Paper
Deep Placental Vessel Segmentation for Fetoscopic Mosaicking
During fetoscopic laser photocoagulation, a treatment for twin-to-twin transfusion syndrome (TTTS), the clinician first identifies abnormal placental vascular connections and laser ablates them to regulate blo...
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Chapter and Conference Paper
The Sixth Visual Object Tracking VOT2018 Challenge Results
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers...
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Chapter and Conference Paper
Hydranet: Data Augmentation for Regression Neural Networks
Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annotator expertise is often scar...
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Chapter and Conference Paper
Generative Adversarial Network for Segmentation of Motion Affected Neonatal Brain MRI
Automatic neonatal brain tissue segmentation in preterm born infants is a prerequisite for evaluation of brain development. However, automatic segmentation is often hampered by motion artifacts caused by infan...
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Chapter and Conference Paper
Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices
In this paper, we present a fully convolutional densely connected network (Tiramisu) for multiple sclerosis (MS) lesion segmentation. Different from existing methods, we use stacked slices from all three anato...
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Chapter and Conference Paper
Optimal Experimental Design for Biophysical Modelling in Multidimensional Diffusion MRI
Computational models of biophysical tissue properties have been widely used in diffusion MRI (dMRI) research to elucidate the link between microstructural properties and MR signal formation. For brain tissue,...
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Chapter and Conference Paper
Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network
Localization of focal vascular lesions on brain MRI is an important component of research on the etiology of neurological disorders. However, manual annotation of lesions can be challenging, time-consuming an...
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Chapter and Conference Paper
Variational AutoEncoder for Regression: Application to Brain Aging Analysis
While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close this gap by proposing a unified...
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Chapter and Conference Paper
Automatic Paraspinal Muscle Segmentation in Patients with Lumbar Pathology Using Deep Convolutional Neural Network
Recent evidence suggests an association between low back pain (LBP) and changes in lumbar paraspinal muscle morphology and composition (i.e., fatty infiltration). Quantitative measurements of muscle cross-sect...
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Chapter and Conference Paper
Fiber Tracking in Traumatic Brain Injury: Comparison of 9 Tractography Algorithms
Traumatic brain injury (TBI) can cause widespread and long-lasting damage to white matter. Diffusion weighted imaging methods are uniquely sensitive to this disruption. Even so, traumatic injury often disrupts...
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Chapter and Conference Paper
Building an Ensemble of Complementary Segmentation Methods by Exploiting Probabilistic Estimates
Two common ways of approaching atlas-based segmentation of brain MRI are (1) intensity-based modelling and (2) multi-atlas label fusion. Intensity-based methods are robust to registration errors but need disti...
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Chapter and Conference Paper
Hyperalignment of Multi-subject fMRI Data by Synchronized Projections
Group analysis of fMRI data via multivariate pattern methods requires accurate alignments between neuronal activities of different subjects in order to attain competitive inter-subject classification rates. Hy...
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Chapter and Conference Paper
Photoacoustic Imaging Paradigm Shift: Towards Using Vendor-Independent Ultrasound Scanners
Photoacoustic (PA) imaging requires channel data acquisition synchronized with a laser firing system. Unfortunately, the access to these channel data is only available on specialized research systems, and most...
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Chapter and Conference Paper
Class-Driven Color Transformation for Semantic Labeling
We propose a novel class-driven color transformation aimed at semantic labeling. In contrast with other approaches elsewhere in the literature, our approach is a supervised one employing class information to l...
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Chapter and Conference Paper
A Straightforward Implementation of a GPU-accelerated ELM in R with NVIDIA Graphic Cards
General purpose computing on graphics processing units (GPGPU) is a promising technique to cope with nowadays arising computational challenges due to the suitability of GPUs for parallel processing. Several li...
