86 Result(s)
within
Chapter
Conference Paper
Dong-**ng Wang
English
Artificial Intelligence
Image Processing and Computer Vision
Pattern Recognition
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Chapter and Conference Paper
Accelerated Lifetime Experiment of Maximum Current Ratio Based on Charge and Discharge Capacity Confinement
Lithium-ion batteries will undergo continuous aging during the process of charging and discharging. Charging and discharging cycle conditions for lithium-ion batteries are usually an important method to detect...
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Chapter and Conference Paper
Visual Realism Assessment for Face-Swap Videos
Deep-learning-based face-swap videos, also known as deepfakes, are becoming more and more realistic and deceiving. The malicious usage of these face-swap videos has caused wide concerns. The research community...
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Chapter and Conference Paper
Adaptive Rounding Compensation for Post-training Quantization
Network quantization can compress and accelerate deep neural networks by reducing the bit-width of network parameters so that the quantized networks can be deployed to resource-limited devices. Post-Training Q...
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Chapter and Conference Paper
Learning a Deep Fourier Attention Generative Adversarial Network for Light Field Image Super-Resolution
Human eyes can see the three-dimensional (3D) world because they receive the light emitted by objects, and the light field (LF) is a complete representation of the set of light in the 3D world. Light field ima...
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Chapter and Conference Paper
Reciprocal Learning for Semi-supervised Segmentation
Semi-supervised learning has been recently employed to solve problems from medical image segmentation due to challenges in acquiring sufficient manual annotations, which is an important prerequisite for buildi...
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Chapter and Conference Paper
Triplet-Branch Network with Prior-Knowledge Embedding for Fatigue Fracture Grading
In recent years, there has been increasing awareness of the occurrence of fatigue fractures. Athletes and soldiers, who engaged in unaccustomed, repetitive or vigorous activities, are potential victims of such...
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Chapter and Conference Paper
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound
Standard plane recognition plays an important role in prenatal ultrasound (US) screening. Automatically recognizing the standard plane along with the corresponding anatomical structures in US image can not onl...
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Chapter and Conference Paper
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures
Building robust deep learning-based models requires diverse training data, ideally from several sources. However, these datasets cannot be combined easily because of patient privacy concerns or regulatory hurd...
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Chapter and Conference Paper
Hierarchical Attention Guided Framework for Multi-resolution Collaborative Whole Slide Image Segmentation
Segmentation of whole slide images (WSIs) is an important step for computer-aided cancer diagnosis. However, due to the gigapixel dimension, WSIs are usually cropped into patches for analysis. Processing high-...
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Chapter and Conference Paper
Frequency Attention Network: Blind Noise Removal for Real Images
With outstanding feature extraction capabilities, deep convolutional neural networks (CNNs) have achieved extraordinary improvements in image denoising tasks. However, because of the difference of statistical ...
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Chapter and Conference Paper
Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation
Computed tomography (CT) reconstruction from X-ray projections acquired within a limited angle range is challenging, especially when the angle range is extremely small. Both analytical and iterative models nee...
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Chapter and Conference Paper
Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification
Rare diseases are characterized by low prevalence and are often chronically debilitating or life-threatening. Imaging-based classification of rare diseases is challenging due to the severe shortage in training...
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Chapter and Conference Paper
Computer-Aided Tumor Diagnosis in Automated Breast Ultrasound Using 3D Detection Network
Automated breast ultrasound (ABUS) is a new and promising imaging modality for breast cancer detection and diagnosis, which could provide intuitive 3D information and coronal plane information with great diagn...
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Chapter and Conference Paper
Adversarial Vision Challenge
This competition was meant to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. It encouraged researchers to develop query-efficient adversa...
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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
Model Learning: Primal Dual Networks for Fast MR Imaging
Magnetic resonance imaging (MRI) is known to be a slow imaging modality and undersampling in k-space has been used to increase the imaging speed. However, image reconstruction from undersampled k-space data is...
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Chapter and Conference Paper
Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound
Standard plane localization is crucial for ultrasound (US) diagnosis. In prenatal US, dozens of standard planes are manually acquired with a 2D probe. It is time-consuming and operator-dependent. In comparison...
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Chapter and Conference Paper
FetusMap: Fetal Pose Estimation in 3D Ultrasound
The 3D ultrasound (US) entrance inspires a multitude of automated prenatal examinations. However, studies about the structuralized description of the whole fetus in 3D US are still rare. In this paper, we prop...
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Chapter and Conference Paper
An Advanced Version of MDNet for Visual Tracking
Tracking-by-detection is an effective framework for visual tracking tasks. For example, the Multi-Domain Convolution Neural Network (MDNet) achieves outstanding results in multiple benchmarks. However, the t...
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Chapter and Conference Paper
Improving Multi-atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation
Multi-atlas segmentation (MAS) is widely used in automatically labeling medical images. The performance of patch-based MAS approaches relies on accurate estimation of local patch similarity, a proxy of the pr...
