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Article
Open AccessCTSN: Predicting cloth deformation for skeleton-based characters with a two-stream skinning network
We present a novel learning method using a two-stream network to predict cloth deformation for skeleton-based characters. The characters processed in our approach are not limited to humans, and can be other ta...
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Article
Open AccessSphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training
3D morphable models (3DMMs) are generative models for face shape and appearance. Recent works impose face recognition constraints on 3DMM shape parameters so that the face shapes of the same person remain cons...
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Chapter and Conference Paper
Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification
Whole Slide Image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. Presently, WSI classification is usually regarded as a Multiple Instanc...
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Article
Open Access3D corrective nose reconstruction from a single image
There is a steadily growing range of applications that can benefit from facial reconstruction techniques, leading to an increasing demand for reconstruction of high-quality 3D face models. While it is an impor...
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Chapter and Conference Paper
Deep Liver Lesion AI System: A Liver Lesion Diagnostic System Using Deep Learning in Multiphase CT
In the medical field, computer-aided diagnosis (CAD) systems are an important tool that can assist doctors in the diagnosis process. However, the powerful higher performance and accuracy models in CAD systems ...
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Chapter
Radiomics and Its Application in Predicting Microvascular Invasion of Hepatocellular Carcinoma
Hepatocellular carcinoma (HCC) is a global health concern, with increasing morbidity and mortality rates in the world. Microvascular invasion (MVI) is a major cause of early postoperative recurrence of HCC. Pr...
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Article
Open AccessAccurate and fast mitotic detection using an anchor-free method based on full-scale connection with recurrent deep layer aggregation in 4D microscopy images
To effectively detect and investigate various cell-related diseases, it is essential to understand cell behaviour. The ability to detection mitotic cells is a fundamental step in diagnosing cell-related diseas...
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Chapter and Conference Paper
Patch-Free 3D Medical Image Segmentation Driven by Super-Resolution Technique and Self-Supervised Guidance
3D medical image segmentation with high resolution is an important issue for accurate diagnosis. The main challenge for this task is its large computational cost and GPU memory restriction. Most of the existin...
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Chapter and Conference Paper
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting
Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods usually fused cross-phase features ...
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Chapter and Conference Paper
G-GCSN: Global Graph Convolution Shrinkage Network for Emotion Perception from Gait
Recently, emotion recognition through gait, which is more difficult to imitate than other biological characteristics, has aroused extensive attention. Although some deep-learning studies have been conducted in...
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Chapter and Conference Paper
3D Graph-S2Net: Shape-Aware Self-ensembling Network for Semi-supervised Segmentation with Bilateral Graph Convolution
Semi-supervised learning (SSL) algorithms have attracted much attentions in medical image segmentation due to challenge in acquiring pixel-wise annotations by using unlabeled data. However, most of existing SS...
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Chapter and Conference Paper
Multimodal Priors Guided Segmentation of Liver Lesions in MRI Using Mutual Information Based Graph Co-Attention Networks
Segmentation of focal liver lesions serves as an essential preprocessing step for initial diagnosis, stage differentiation, and post-treatment efficacy evaluation. Multimodal MRI scans (e.g., T1WI, T2WI) provi...
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Chapter
Multi-scale Deep Convolutional Neural Networks for Emphysema Classification and Quantification
In this work, we aim at classification and quantification of emphysema in computed tomography (CT) images of lungs. Most previous works are limited to extracting low-level features or mid-level features withou...
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Article
Joined fragment segmentation for fractured bones using GPU-accelerated shape-preserving erosion and dilation
Joined fragment segmentation for fractured bones segmented from CT (computed tomography) images is a time-consuming task and calls for lots of interactions. To alleviate segmentation burdens of radiologists, w...
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Article
Open AccessA three-stage real-time detector for traffic signs in large panoramas
Traffic sign detection is one of the key components in autonomous driving. Advanced autonomous vehicles armed with high quality sensors capture high definition images for further analysis. Detecting traffic si...
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Article
Expressive facial style transfer for personalized memes mimic
Meme, usually represented by an image of exaggerated expressive face captioned with short text, are increasingly produced and used online to express people’s strong or subtle emotions. Meanwhile, meme mimic ap...
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Chapter and Conference Paper
A Cascade Attention Network for Liver Lesion Classification in Weakly-Labeled Multi-phase CT Images
Focal liver lesion classification is important to the diagnostics of liver disease. In clinics, lesion type is usually determined by multi-phase contrast-enhanced CT images. Previous methods of automatic live...
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Chapter and Conference Paper
Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Prior
Medical image segmentation is one of the most important steps in computer-aided intervention and diagnosis. Although deep learning-based segmentation methods have achieved great success in computer vision doma...
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Chapter and Conference Paper
Multiphase Focal Liver Lesions Classification with Combined N-gram and BoVW
The bag-of-visual-words (BoVW) model has emerged as an effective approach to represent features for focal liver lesions (FLLs). However, most of the previous methods have the limitation of insufficient conside...
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Chapter and Conference Paper
A Semantic Parametric Model for 3D Human Body Resha**
Semantic human body resha** builds a 3D body according to several anthropometric measurements, playing important roles in virtual fitting and human body design. We propose a novel part-based semantic body mo...
