500 Result(s)
within
Image Processing and Computer Vision
Pattern Recognition
**lu Chen
Chapter
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Chapter and Conference Paper
Correction to: Real-Time FPGA Design for OMP Targeting 8K Image Reconstruction
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Chapter and Conference Paper
Abstract: Maximum A-posteriori Signal Recovery for OCT Angiography Image Generation
Optical coherence tomography angiography (OCTA) is a clinically promising modality to image retinal vasculature. For this end, optical coherence tomography (OCT) volumes are repeatedly scanned and intensity ch...
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Chapter and Conference Paper
Abstract: Automatic Dementia Screening and Scoring by Applying Deep Learning on Clock-drawing Tests
Dementia is one of the most common neurological syndromes in the world. Usually, diagnoses are made based on paper-and-pencil tests and scored by personal judgments of experts. This technique can introduce err...
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Chapter and Conference Paper
Reading Digital Video Clocks by Two Phases of Connected Deep Networks
This paper presents an algorithm for reading digital video clocks by using two phases of connected deep networks to avoid the demerits of existing heuristic algorithms. The problem of reading digital video clo...
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Chapter and Conference Paper
Correction to: Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network
The original version of this chapter was revised. The NIH grant number has been corrected to EB006733 and typographical errors were corrected.
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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
Holistic Brain Tumor Screening and Classification Based on DenseNet and Recurrent Neural Network
We present a holistic brain tumor screening and classification method for detecting and distinguishing multiple types of brain tumors on MR images. The challenges arise from the significant variations of locat...
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Chapter and Conference Paper
Correction to: Investigating the Role of VR in a Simulation-Based Medical Planning System for Coronary Interventions
The original version of this chapter was revised. The spelling of the last author’s name was corrected to Amanda Randles.
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Chapter and Conference Paper
MixNet: Multi-modality Mix Network for Brain Segmentation
Automated brain structure segmentation is important to many clinical quantitative analysis and diagnoses. In this work, we introduce MixNet, a 2D semantic-wise deep convolutional neural network to segment brai...
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Chapter and Conference Paper
S3D-UNet: Separable 3D U-Net for Brain Tumor Segmentation
Brain tumor is one of the leading causes of cancer death. Accurate segmentation and quantitative analysis of brain tumor are critical for diagnosis and treatment planning. Since manual segmentation is time-con...
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Chapter and Conference Paper
Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study
In this paper a comparative study is presented on dynamic prediction of customer profitability over time. Customer profitability is measured by Recency, Frequency, and Monetary (RFM) model. A real transactiona...
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Chapter and Conference Paper
Learning Contextual and Attentive Information for Brain Tumor Segmentation
Thanks to the powerful representation learning ability, convolutional neural network has been an effective tool for the brain tumor segmentation task. In this work, we design multiple deep architectures of var...
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Chapter and Conference Paper
Brain Tumor Segmentation on Multimodal MR Imaging Using Multi-level Upsampling in Decoder
Accurate brain tumor segmentation plays a pivotal role in clinical practice and research settings. In this paper, we propose the multi-level up-sampling network (MU-Net) to learn the image presentations of tra...
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Chapter and Conference Paper
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction
This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellatio...
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Chapter and Conference Paper
cGAN-Based Lacquer Cracks Segmentation in ICGA Image
The increasing prevalence of high myopia has raised concern worldwide. In high myopia, myopia macular degeneration (MMD) is a major cause of vision impairment and lacquer crack (LC) is one of the main signs of...
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Chapter and Conference Paper
Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-phase CT Images
Computer-aided diagnosis (CAD) systems are useful for assisting radiologists with clinical diagnoses by classifying focal liver lesions (FLLs) based on multi-phase computed tomography (CT) images. Although man...
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Chapter and Conference Paper
Deep Active Self-paced Learning for Accurate Pulmonary Nodule Segmentation
Automatic and accurate pulmonary nodule segmentation in lung Computed Tomography (CT) volumes plays an important role in computer-aided diagnosis of lung cancer. However, this task is challenging due to target...
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Chapter and Conference Paper
A Framework for Identifying Diabetic Retinopathy Based on Anti-noise Detection and Attention-Based Fusion
Automatic diagnosis of diabetic retinopathy (DR) using retinal fundus images is a challenging problem because images of low grade DR may contain only a few tiny lesions which are difficult to perceive even to ...
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Chapter and Conference Paper
ReenactGAN: Learning to Reenact Faces via Boundary Transfer
We present a novel learning-based framework for face reenactment. The proposed method, known as ReenactGAN, is capable of transferring facial movements and expressions from an arbitrary person’s monocular vide...
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Chapter and Conference Paper
Identifying Brain Networks of Multiple Time Scales via Deep Recurrent Neural Network
For decades, task-based functional magnetic resonance imaging (tfMRI) has been a powerful noninvasive tool to explore the organizational architecture of human brain function. Researchers have developed a varie...
