Search Results for author: Yuan Xue

Found 38 papers, 13 papers with code

Securing Deep Generative Models with Universal Adversarial Signature

1 code implementation25 May 2023 Yu Zeng, Mo Zhou, Yuan Xue, Vishal M. Patel

Prior research attempted to mitigate these threats by detecting generated images, but the varying traces left by different generative models make it challenging to create a universal detector capable of generalizing to new, unseen generative models.

The First Comprehensive Dataset with Multiple Distortion Types for Visual Just-Noticeable Differences

no code implementations5 Mar 2023 Yaxuan Liu, Jian Jin, Yuan Xue, Weisi Lin

To benefit JND modeling, this work establishes a generalized JND dataset with a coarse-to-fine JND selection, which contains 106 source images and 1, 642 JND maps, covering 25 distortion types.

Image Quality Assessment Video Compression

Automated Ventricle Parcellation and Evan's Ratio Computation in Pre- and Post-Surgical Ventriculomegaly

no code implementations3 Mar 2023 Yuli Wang, Anqi Feng, Yuan Xue, Lianrui Zuo, Yihao Liu, Ari M. Blitz, Mark G. Luciano, Aaron Carass, Jerry L. Prince

Normal pressure hydrocephalus~(NPH) is a brain disorder associated with enlarged ventricles and multiple cognitive and motor symptoms.


A latent space for unsupervised MR image quality control via artifact assessment

no code implementations1 Feb 2023 Lianrui Zuo, Yuan Xue, Blake E. Dewey, Yihao Liu, Jerry L. Prince, Aaron Carass

Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images.

Contrastive Learning

Segmenting thalamic nuclei from manifold projections of multi-contrast MRI

no code implementations15 Jan 2023 Chang Yan, Muhan Shao, Zhangxing Bian, Anqi Feng, Yuan Xue, Jiachen Zhuo, Rao P. Gullapalli, Aaron Carass, Jerry L. Prince

After registration of these contrasts and isolation of the thalamus, we use the uniform manifold approximation and projection (UMAP) method for dimensionality reduction to produce a low-dimensional representation of the data within the thalamus.

Dimensionality Reduction

Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment

2 code implementations14 Oct 2022 Haomiao Ni, Yuan Xue, Liya Ma, Qian Zhang, Xiaoye Li, Xiaolei Huang

We collected a new clinical IMV dataset with GMA annotations, and our experiments show that SPN models for body parsing and pose estimation trained on the first two datasets generalize well to the new clinical dataset and their results can significantly boost the CRNN-based GMA prediction performance.

Data Augmentation Pose Estimation

Cross-identity Video Motion Retargeting with Joint Transformation and Synthesis

1 code implementation2 Oct 2022 Haomiao Ni, Yihao Liu, Sharon X. Huang, Yuan Xue

The novel design of dual branches combines the strengths of deformation-grid-based transformation and warp-free generation for better identity preservation and robustness to occlusion in the synthesized videos.

motion retargeting

DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement

no code implementations14 Sep 2022 Yonglong Jiang, Liangliang Li, Yuan Xue, Hongbing Ma

Solving the problem of poor low-light image quality can effectively improve the visual quality of images and better improve the usability of computer vision.

Denoising Image Generation +1

Deep filter bank regression for super-resolution of anisotropic MR brain images

no code implementations6 Sep 2022 Samuel W. Remedios, Shuo Han, Yuan Xue, Aaron Carass, Trac D. Tran, Dzung L. Pham, Jerry L. Prince

In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane signals are typically of lower resolution than the in-plane signals.

regression Super-Resolution

HVS-Inspired Signal Degradation Network for Just Noticeable Difference Estimation

1 code implementation16 Aug 2022 Jian Jin, Yuan Xue, Xingxing Zhang, Lili Meng, Yao Zhao, Weisi Lin

However, they have a major drawback that the generated JND is assessed in the real-world signal domain instead of in the perceptual domain in the human brain.

End-to-end Graph-constrained Vectorized Floorplan Generation with Panoptic Refinement

no code implementations27 Jul 2022 Jiachen Liu, Yuan Xue, Jose Duarte, Krishnendra Shekhawat, Zihan Zhou, Xiaolei Huang

In the first stage, we encode the room connectivity graph input by users with a graph convolutional network (GCN), then apply an autoregressive transformer network to generate an initial floorplan sequence.

Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans

1 code implementation30 Jun 2022 Haomiao Ni, Yuan Xue, Kelvin Wong, John Volpi, Stephen T. C. Wong, James Z. Wang, Xiaolei Huang

In this paper, we propose a novel Asymmetry Disentanglement Network (ADN) to automatically separate pathological asymmetries and intrinsic anatomical asymmetries in NCCTs for more effective and interpretable AIS segmentation.


Disentangling A Single MR Modality

no code implementations10 May 2022 Lianrui Zuo, Yihao Liu, Yuan Xue, Shuo Han, Murat Bilgel, Susan M. Resnick, Jerry L. Prince, Aaron Carass

Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks.

Anatomy Disentanglement +3

Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach

1 code implementation29 Apr 2022 Shuzhao Xie, Yuan Xue, Yifei Zhu, Zhi Wang

With the advancement of deep learning techniques, major cloud providers and niche machine learning service providers start to offer their cloud-based machine learning tools, also known as machine learning as a service (MLaaS), to the public.

BIG-bench Machine Learning reinforcement-learning +1

Coordinate Translator for Learning Deformable Medical Image Registration

1 code implementation5 Mar 2022 Yihao Liu, Lianrui Zuo, Shuo Han, Yuan Xue, Jerry L. Prince, Aaron Carass

The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images.

Deformable Medical Image Registration Image Registration +1

Neural Stochastic Dual Dynamic Programming

no code implementations ICLR 2022 Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai

Stochastic dual dynamic programming (SDDP) is a state-of-the-art method for solving multi-stage stochastic optimization, widely used for modeling real-world process optimization tasks.

Stochastic Optimization

A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis

1 code implementation10 Nov 2021 Jiarong Ye, Yuan Xue, Peter Liu, Richard Zaino, Keith Cheng, Xiaolei Huang

Generative models have been applied in the medical imaging domain for various image recognition and synthesis tasks.

Contrastive Learning Image Generation

Deep Image Synthesis from Intuitive User Input: A Review and Perspectives

no code implementations9 Jul 2021 Yuan Xue, Yuan-Chen Guo, Han Zhang, Tao Xu, Song-Hai Zhang, Xiaolei Huang

In many applications of computer graphics, art and design, it is desirable for a user to provide intuitive non-image input, such as text, sketch, stroke, graph or layout, and have a computer system automatically generate photo-realistic images that adhere to the input content.

Image Generation Image Retrieval +1

Learning to Select Best Forecast Tasks for Clinical Outcome Prediction

no code implementations NeurIPS 2020 Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai

The paradigm of pretraining' from a set of relevant auxiliary tasks and thenfinetuning' on a target task has been successfully applied in many different domains.


Synthetic Sample Selection via Reinforcement Learning

no code implementations26 Aug 2020 Jiarong Ye, Yuan Xue, L. Rodney Long, Sameer Antani, Zhiyun Xue, Keith Cheng, Xiaolei Huang

However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images.

Data Augmentation Image Classification +2

SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos

1 code implementation16 Jul 2020 Haomiao Ni, Yuan Xue, Qian Zhang, Xiaolei Huang

In this paper, we propose a semi-supervised body parsing model, termed SiamParseNet (SPN), to jointly learn single frame body parsing and label propagation between frames in a semi-supervised fashion.

Learning Unstable Dynamical Systems with Time-Weighted Logarithmic Loss

no code implementations10 Jul 2020 Kamil Nar, Yuan Xue, Andrew M. Dai

When training the parameters of a linear dynamical model, the gradient descent algorithm is likely to fail to converge if the squared-error loss is used as the training loss function.

Semi-Supervised Cervical Dysplasia Classification With Learnable Graph Convolutional Network

no code implementations1 Apr 2020 Yanglan Ou, Yuan Xue, Ye Yuan, Tao Xu, Vincent Pisztora, Jia Li, Xiaolei Huang

In this paper, we propose a novel and more flexible GCN model with a feature encoder that adaptively updates the adjacency matrix during learning and demonstrate that this model design leads to improved performance.

Classification General Classification

Selective Synthetic Augmentation with Quality Assurance

no code implementations9 Dec 2019 Yuan Xue, Jiarong Ye, Rodney Long, Sameer Antani, Zhiyun Xue, Xiaolei Huang

To mitigate these issues, we investigate a novel data augmentation pipeline that selectively adds new synthetic images generated by conditional Adversarial Networks (cGANs), rather than extending directly the training set with synthetic images.

Classification Data Augmentation +2

Neural Wireframe Renderer: Learning Wireframe to Image Translations

1 code implementation ECCV 2020 Yuan Xue, Zihan Zhou, Xiaolei Huang

To this end, we propose a novel model based on a structure-appearance joint representation learned from both images and wireframes.

Image Generation Translation

Shape-Aware Organ Segmentation by Predicting Signed Distance Maps

no code implementations9 Dec 2019 Yuan Xue, Hui Tang, Zhi Qiao, Guanzhong Gong, Yong Yin, Zhen Qian, Chao Huang, Wei Fan, Xiaolei Huang

In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness.

Hippocampus Organ Segmentation

Deep Physiological State Space Model for Clinical Forecasting

no code implementations4 Dec 2019 Yuan Xue, Denny Zhou, Nan Du, Andrew Dai, Zhen Xu, Kun Zhang, Claire Cui

Clinical forecasting based on electronic medical records (EMR) can uncover the temporal correlations between patients' conditions and outcomes from sequences of longitudinal clinical measurements.

Modelling EHR timeseries by restricting feature interaction

no code implementations14 Nov 2019 Kun Zhang, Yuan Xue, Gerardo Flores, Alvin Rajkomar, Claire Cui, Andrew M. Dai

Time series data are prevalent in electronic health records, mostly in the form of physiological parameters such as vital signs and lab tests.

Mortality Prediction Time Series Analysis

Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer

2 code implementations11 Jun 2019 Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, Andrew M. Dai

A recent study showed that using the graphical structure underlying EHR data (e. g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure prediction.

Graph Reconstruction Readmission Prediction +1

Thoracic Disease Identification and Localization with Limited Supervision

1 code implementation CVPR 2018 Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-Jia Li, Li Fei-Fei

Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning.

General Classification

SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation

2 code implementations6 Jun 2017 Yuan Xue, Tao Xu, Han Zhang, Rodney Long, Xiaolei Huang

Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.

Brain Tumor Segmentation Image Segmentation +1

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