Search Results for author: Terrence Chen

Found 41 papers, 3 papers with code

Disguise without Disruption: Utility-Preserving Face De-Identification

no code implementations23 Mar 2023 Zikui Cai, Zhongpai Gao, Benjamin Planche, Meng Zheng, Terrence Chen, M. Salman Asif, Ziyan Wu

While a variety of solutions have been proposed to de-identify such images, they often corrupt other non-identifying facial attributes that would be relevant for downstream tasks.

De-identification Ensemble Learning

Exploring Cycle Consistency Learning in Interactive Volume Segmentation

1 code implementation11 Mar 2023 Qin Liu, Meng Zheng, Benjamin Planche, Zhongpai Gao, Terrence Chen, Marc Niethammer, Ziyan Wu

To this end, we introduce a backward segmentation path that propagates the intermediate segmentation back to the starting slice using the same propagation network.

An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal

no code implementations6 Feb 2023 Yikang Liu, Eric Z. Chen, Xiao Chen, Terrence Chen, Shanhui Sun

Previous studies tackled these two problems separately, where super resolution methods tend to enhance Gibbs artifacts, whereas Gibbs ringing removal methods tend to blur the images.


Computationally Efficient 3D MRI Reconstruction with Adaptive MLP

no code implementations21 Jan 2023 Eric Z. Chen, Chi Zhang, Xiao Chen, Yikang Liu, Terrence Chen, Shanhui Sun

Recon3DMLP improves HR 3D reconstruction and outperforms several existing CNN-based models under similar GPU memory consumption, which demonstrates that Recon3DMLP is a practical solution for HR 3D MRI reconstruction.

3D Reconstruction MRI Reconstruction

Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstruction

no code implementations3 Jan 2023 Daniel H. Pak, Xiao Chen, Eric Z. Chen, Yikang Liu, Terrence Chen, Shanhui Sun

Deep learning (DL)-based methods have shown promising results for single-slice MR reconstruction, but the addition of SMS acceleration raises unique challenges due to the composite k-space signals and the resulting images with strong inter-slice artifacts.

MRI Reconstruction Transfer Learning

Progressive Multi-view Human Mesh Recovery with Self-Supervision

no code implementations10 Dec 2022 Xuan Gong, Liangchen Song, Meng Zheng, Benjamin Planche, Terrence Chen, Junsong Yuan, David Doermann, Ziyan Wu

To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e. g., motion capture, sport analysis) and robustness to single-view ambiguities.

Benchmarking Human Mesh Recovery

JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI

no code implementations22 Oct 2022 Lin Zhao, Xiao Chen, Eric Z. Chen, Yikang Liu, Dinggang Shen, Terrence Chen, Shanhui Sun

The proposed framework consists of a sampling mask generator for each image contrast and a reconstructor exploiting the inter-contrast correlations with a recurrent structure which enables the information sharing in a holistic way.

Forecasting Human Trajectory from Scene History

1 code implementation17 Oct 2022 Mancheng Meng, Ziyan Wu, Terrence Chen, Xiran Cai, Xiang Sean Zhou, Fan Yang, Dinggang Shen

We categorize scene history information into two types: historical group trajectory and individual-surroundings interaction.

Trajectory Prediction

Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation

no code implementations10 Sep 2022 Xuan Gong, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, Arun Innanje

Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively train a central model while the training data remains decentralized.

Federated Learning Image Classification +4

Self-supervised Human Mesh Recovery with Cross-Representation Alignment

no code implementations10 Sep 2022 Xuan Gong, Meng Zheng, Benjamin Planche, Srikrishna Karanam, Terrence Chen, David Doermann, Ziyan Wu

However, on synthetic dense correspondence maps (i. e., IUV) few have been explored since the domain gap between synthetic training data and real testing data is hard to address for 2D dense representation.

Human Mesh Recovery

Deep Statistic Shape Model for Myocardium Segmentation

no code implementations21 Jul 2022 Xiaoling Hu, Xiao Chen, Yikang Liu, Eric Z. Chen, Terrence Chen, Shanhui Sun

Additionally, the predicted point cloud guarantees boundary correspondence for sequential images, which contributes to the downstream tasks, such as the motion estimation of myocardium.

Motion Estimation Myocardium Segmentation

PseudoClick: Interactive Image Segmentation with Click Imitation

no code implementations12 Jul 2022 Qin Liu, Meng Zheng, Benjamin Planche, Srikrishna Karanam, Terrence Chen, Marc Niethammer, Ziyan Wu

The goal of click-based interactive image segmentation is to obtain precise object segmentation masks with limited user interaction, i. e., by a minimal number of user clicks.

Image Segmentation Semantic Segmentation

Invertible Sharpening Network for MRI Reconstruction Enhancement

no code implementations6 Jun 2022 Siyuan Dong, Eric Z. Chen, Lin Zhao, Xiao Chen, Yikang Liu, Terrence Chen, Shanhui Sun

During inference, the learned blurring transform can be inverted to a sharpening transform leveraging the network's invertibility.

MRI Reconstruction SSIM

SMPL-A: Modeling Person-Specific Deformable Anatomy

no code implementations CVPR 2022 Hengtao Guo, Benjamin Planche, Meng Zheng, Srikrishna Karanam, Terrence Chen, Ziyan Wu

In order to obtain accurate target location information, clinicians have to either conduct frequent intraoperative scans, resulting in higher exposition of patients to radiations, or adopt proxy procedures (e. g., creating and using custom molds to keep patients in the exact same pose during both preoperative organ scanning and subsequent treatment.

Anatomy Human Mesh Recovery

Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis

1 code implementation23 Dec 2021 Xi Ouyang, Srikrishna Karanam, Ziyan Wu, Terrence Chen, Jiayu Huo, Xiang Sean Zhou, Qian Wang, Jie-Zhi Cheng

However, doing this accurately will require a large amount of disease localization annotations by clinical experts, a task that is prohibitively expensive to accomplish for most applications.

Decision Making

Learning Local Recurrent Models for Human Mesh Recovery

no code implementations27 Jul 2021 Runze Li, Srikrishna Karanam, Ren Li, Terrence Chen, Bir Bhanu, Ziyan Wu

We conduct a variety of experiments on standard video mesh recovery benchmark datasets such as Human3. 6M, MPI-INF-3DHP, and 3DPW, demonstrating the efficacy of our design of modeling local dynamics as well as establishing state-of-the-art results based on standard evaluation metrics.

Ranked #4 on 3D Human Pose Estimation on MPI-INF-3DHP (PA-MPJPE metric)

3D Human Pose Estimation 3D Human Shape Estimation +1

Spatio-Temporal Representation Factorization for Video-based Person Re-Identification

no code implementations ICCV 2021 Abhishek Aich, Meng Zheng, Srikrishna Karanam, Terrence Chen, Amit K. Roy-Chowdhury, Ziyan Wu

To alleviate these problems, we propose Spatio-Temporal Representation Factorization (STRF), a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID.

Video-Based Person Re-Identification

Everybody Is Unique: Towards Unbiased Human Mesh Recovery

no code implementations13 Jul 2021 Ren Li, Meng Zheng, Srikrishna Karanam, Terrence Chen, Ziyan Wu

Next, we present a simple baseline to address this problem that is scalable and can be easily used in conjunction with existing algorithms to improve their performance.

 Ranked #1 on 3D Human Shape Estimation on SSP-3D (PVE-T metric)

3D Human Pose Estimation 3D Human Shape Estimation +1

Multi-scale Neural ODEs for 3D Medical Image Registration

no code implementations16 Jun 2021 Junshen Xu, Eric Z. Chen, Xiao Chen, Terrence Chen, Shanhui Sun

The inference consists of iterative gradient updates similar to a conventional gradient descent optimizer but in a much faster way, because the neural ODE learns from the training data to adapt the gradient efficiently at each iteration.

Image Registration Medical Image Registration

Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning

no code implementations17 May 2021 Eric Z. Chen, Yongquan Ye, Xiao Chen, Jingyuan Lyu, Zhongqi Zhang, Yichen Hu, Terrence Chen, Jian Xu, Shanhui Sun

We propose a deep learning framework for undersampled 3D MRI data reconstruction and apply it to MULTIPLEX MRI.

MRI Reconstruction

Cardiac Functional Analysis with Cine MRI via Deep Learning Reconstruction

no code implementations17 May 2021 Eric Z. Chen, Xiao Chen, Jingyuan Lyu, Qi Liu, Zhongqi Zhang, Yu Ding, Shuheng Zhang, Terrence Chen, Jian Xu, Shanhui Sun

To the best of our knowledge, this is the first work to evaluate the cine MRI with deep learning reconstruction for cardiac function analysis and compare it with other conventional methods.

Ensemble Attention Distillation for Privacy-Preserving Federated Learning

no code implementations ICCV 2021 Xuan Gong, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, Arun Innanje

Such decentralized training naturally leads to issues of imbalanced or differing data distributions among the local models and challenges in fusing them into a central model.

Federated Learning Privacy Preserving

Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks

no code implementations25 Aug 2020 Qiaoying Huang, Eric Z. Chen, Hanchao Yu, Yimo Guo, Terrence Chen, Dimitris Metaxas, Shanhui Sun

We also analyze thickness patterns on different cardiac pathologies with a standard clinical model and the results demonstrate the potential clinical value of our method for thickness based cardiac disease diagnosis.

Anatomy-Aware Cardiac Motion Estimation

no code implementations17 Aug 2020 Pingjun Chen, Xiao Chen, Eric Z. Chen, Hanchao Yu, Terrence Chen, Shanhui Sun

A baseline dense motion tracker is trained to approximate the motion fields and then refined to estimate anatomy-aware motion fields under the weak supervision from the VAE.

Anatomy Motion Estimation

Towards Visually Explaining Similarity Models

no code implementations13 Aug 2020 Meng Zheng, Srikrishna Karanam, Terrence Chen, Richard J. Radke, Ziyan Wu

We show that the resulting similarity models perform, and can be visually explained, better than the corresponding baseline models trained without these constraints.

Image Retrieval Metric Learning +3

Real-Time Cardiac Cine MRI with Residual Convolutional Recurrent Neural Network

no code implementations12 Aug 2020 Eric Z. Chen, Xiao Chen, Jingyuan Lyu, Yuan Zheng, Terrence Chen, Jian Xu, Shanhui Sun

Real-time cardiac cine MRI does not require ECG gating in the data acquisition and is more useful for patients who can not hold their breaths or have abnormal heart rhythms.

Image Reconstruction

Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation

no code implementations28 Jun 2020 Hanchao Yu, Xiao Chen, Humphrey Shi, Terrence Chen, Thomas S. Huang, Shanhui Sun

In this paper, we propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation.

Knowledge Distillation Motion Estimation

MRI Image Reconstruction via Learning Optimization Using Neural ODEs

no code implementations24 Jun 2020 Eric Z. Chen, Terrence Chen, Shanhui Sun

We investigate several models based on three ODE solvers and compare models with fixed solvers and learned solvers.

MRI Reconstruction

FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation

no code implementations CVPR 2020 Hanchao Yu, Shanhui Sun, Haichao Yu, Xiao Chen, Honghui Shi, Thomas Huang, Terrence Chen

In clinical deployment, however, they suffer dramatic performance drops due to mismatched distributions between training and testing datasets, commonly encountered in the clinical environment.

Anatomy Motion Estimation

Hierarchical Kinematic Human Mesh Recovery

no code implementations ECCV 2020 Georgios Georgakis, Ren Li, Srikrishna Karanam, Terrence Chen, Jana Kosecka, Ziyan Wu

In this work, we address this gap by proposing a new technique for regression of human parametric model that is explicitly informed by the known hierarchical structure, including joint interdependencies of the model.

Human Mesh Recovery regression

Pyramid Convolutional RNN for MRI Image Reconstruction

no code implementations2 Dec 2019 Eric Z. Chen, Puyang Wang, Xiao Chen, Terrence Chen, Shanhui Sun

We evaluate our model on the fastMRI knee and brain datasets and the results show that the proposed model outperforms other methods and can recover more details.

MRI Reconstruction

Visual Similarity Attention

no code implementations18 Nov 2019 Meng Zheng, Srikrishna Karanam, Terrence Chen, Richard J. Radke, Ziyan Wu

While there has been substantial progress in learning suitable distance metrics, these techniques in general lack transparency and decision reasoning, i. e., explaining why the input set of images is similar or dissimilar.

Image Retrieval Person Re-Identification +2

Structure-Aware Shape Synthesis

no code implementations4 Aug 2018 Elena Balashova, Vivek Singh, Jiangping Wang, Brian Teixeira, Terrence Chen, Thomas Funkhouser

We propose a new procedure to guide training of a data-driven shape generative model using a structure-aware loss function.

Generating Synthetic X-ray Images of a Person from the Surface Geometry

no code implementations CVPR 2018 Brian Teixeira, Vivek Singh, Terrence Chen, Kai Ma, Birgi Tamersoy, Yifan Wu, Elena Balashova, Dorin Comaniciu

Furthermore, the synthetic X-ray image is parametrized and can be manipulated by adjusting a set of body markers which are also generated during the X-ray image prediction.

Anatomy Anomaly Detection

Deep Decision Network for Multi-Class Image Classification

no code implementations CVPR 2016 Venkatesh N. Murthy, Vivek Singh, Terrence Chen, R. Manmatha, Dorin Comaniciu

During the learning phase, starting from the root network node, DDN automatically builds a network that splits the data into disjoint clusters of classes which would be handled by the subsequent expert networks.

Classification General Classification +1

BodyPrint: Pose Invariant 3D Shape Matching of Human Bodies

no code implementations ICCV 2015 Jiangping Wang, Kai Ma, Vivek Kumar Singh, Thomas Huang, Terrence Chen

3D human body shape matching has large potential on many real world applications, especially with the recent advances in the 3D range sensing technology.

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