Search Results for author: Jinwei Zhang

Found 22 papers, 5 papers with code

Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion

no code implementations3 Dec 2023 Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Dzung L. Pham, Aaron Carass, Jerry L. Prince

Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation.

Lesion Segmentation Segmentation

Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation

no code implementations31 Oct 2023 Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Savannah P. Hays, Dzung L. Pham, Jerry L. Prince, Aaron Carass

Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation.

Domain Generalization Lesion Segmentation

DAGrid: Directed Accumulator Grid

1 code implementation5 Jun 2023 Hang Zhang, Renjiu Hu, Xiang Chen, Rongguang Wang, Jinwei Zhang, Jiahao Li

Specifically, the network incorporating DAGrid has realized a 70. 8% reduction in network parameter size and a 96. 8% decrease in FLOPs, while concurrently improving the Dice score for skin lesion segmentation by 1. 0% compared to state-of-the-art transformers.

Image Registration Lesion Segmentation +1

Physics-based network fine-tuning for robust quantitative susceptibility mapping from high-pass filtered phase

no code implementations5 May 2023 Jinwei Zhang, Alexey Dimov, Chao Li, Hang Zhang, Thanh D. Nguyen, Pascal Spincemaille, Yi Wang

Purpose: To improve the generalization ability of convolutional neural network (CNN) based prediction of quantitative susceptibility mapping (QSM) from high-pass filtered phase (HPFP) image.

SSIM

mcLARO: Multi-Contrast Learned Acquisition and Reconstruction Optimization for simultaneous quantitative multi-parametric mapping

no code implementations7 Apr 2023 Jinwei Zhang, Thanh D. Nguyen, Eddy Solomon, Chao Li, Qihao Zhang, Jiahao Li, Hang Zhang, Pascal Spincemaille, Yi Wang

Results: The retrospective ablation study showed improved image sharpness of mcLARO compared to the baseline network without multi-contrast sampling pattern optimization or image feature fusion, and negligible bias and narrow 95% limits of agreement on regional T1, T2, T2* and QSM values were obtained by the under-sampled reconstructions compared to the fully sampled reconstruction.

Image Reconstruction

DeDA: Deep Directed Accumulator

1 code implementation15 Mar 2023 Hang Zhang, Rongguang Wang, Renjiu Hu, Jinwei Zhang, Jiahao Li

Chronic active multiple sclerosis lesions, also termed as rim+ lesions, can be characterized by a hyperintense rim at the edge of the lesion on quantitative susceptibility maps.

Spatially Covariant Lesion Segmentation

no code implementations19 Jan 2023 Hang Zhang, Rongguang Wang, Jinwei Zhang, Dongdong Liu, Chao Li, Jiahao Li

Compared to natural images, medical images usually show stronger visual patterns and therefore this adds flexibility and elasticity to resource-limited clinical applications by injecting proper priors into neural networks.

Computational Efficiency Lesion Segmentation +2

LARO: Learned Acquisition and Reconstruction Optimization to accelerate Quantitative Susceptibility Mapping

1 code implementation1 Nov 2022 Jinwei Zhang, Pascal Spincemaille, Hang Zhang, Thanh D. Nguyen, Chao Li, Jiahao Li, Ilhami Kovanlikaya, Mert R. Sabuncu, Yi Wang

In this paper, we present our new framework, called Learned Acquisition and Reconstruction Optimization (LARO), which aims to accelerate the multi-echo gradient echo (mGRE) pulse sequence for QSM.

Motion Artifact Reduction in Quantitative Susceptibility Mapping using Deep Neural Network

no code implementations4 May 2021 Chao Li, Hang Zhang, Jinwei Zhang, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang

An approach to reduce motion artifacts in Quantitative Susceptibility Mapping using deep learning is proposed.

Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction

no code implementations10 Mar 2021 Jinwei Zhang, Hang Zhang, Chao Li, Pascal Spincemaille, Mert Sabuncu, Thanh D. Nguyen, Yi Wang

Quantitative imaging in MRI usually involves acquisition and reconstruction of a series of images at multi-echo time points, which possibly requires more scan time and specific reconstruction technique compared to conventional qualitative imaging.

Image Reconstruction

NeRD: Neural Representation of Distribution for Medical Image Segmentation

1 code implementation6 Mar 2021 Hang Zhang, Rongguang Wang, Jinwei Zhang, Chao Li, Gufeng Yang, Pascal Spincemaille, Thanh Nguyen, Yi Wang

We introduce Neural Representation of Distribution (NeRD) technique, a module for convolutional neural networks (CNNs) that can estimate the feature distribution by optimizing an underlying function mapping image coordinates to the feature distribution.

Image Segmentation Lesion Segmentation +2

Geometric Loss for Deep Multiple Sclerosis lesion Segmentation

no code implementations29 Sep 2020 Hang Zhang, Jinwei Zhang, Rongguang Wang, Qihao Zhang, Susan A. Gauthier, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang

Multiple sclerosis (MS) lesions occupy a small fraction of the brain volume, and are heterogeneous with regards to shape, size and locations, which poses a great challenge for training deep learning based segmentation models.

Lesion Segmentation Segmentation

Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentation

no code implementations13 Sep 2020 Hang Zhang, Jinwei Zhang, Rongguang Wang, Qihao Zhang, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang

Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been further designed to capture global contextual information for performance enhancement.

Computational Efficiency Image Reconstruction +1

Probabilistic Dipole Inversion for Adaptive Quantitative Susceptibility Mapping

no code implementations7 Sep 2020 Jinwei Zhang, Hang Zhang, Mert Sabuncu, Pascal Spincemaille, Thanh Nguyen, Yi Wang

A learning-based posterior distribution estimation method, Probabilistic Dipole Inversion (PDI), is proposed to solve the quantitative susceptibility mapping (QSM) inverse problem in MRI with uncertainty estimation.

Density Estimation

Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI

no code implementations28 Jul 2020 Jinwei Zhang, Hang Zhang, Alan Wang, Qihao Zhang, Mert Sabuncu, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang

The previously established LOUPE (Learning-based Optimization of the Under-sampling Pattern) framework for optimizing the k-space sampling pattern in MRI was extended in three folds: firstly, fully sampled multi-coil k-space data from the scanner, rather than simulated k-space data from magnitude MR images in LOUPE, was retrospectively under-sampled to optimize the under-sampling pattern of in-vivo k-space data; secondly, binary stochastic k-space sampling, rather than approximate stochastic k-space sampling of LOUPE during training, was applied together with a straight-through (ST) estimator to estimate the gradient of the threshold operation in a neural network; thirdly, modified unrolled optimization network, rather than modified U-Net in LOUPE, was used as the reconstruction network in order to reconstruct multi-coil data properly and reduce the dependency on training data.

Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle

no code implementations16 Jul 2020 Xiaowei Guo, Teng Liu, Bangbei Tang, Xiaolin Tang, Jinwei Zhang, Wenhao Tan, Shufeng Jin

This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL).

energy management Management +3

Driving Conditions-Driven Energy Management for Hybrid Electric Vehicles: A Review

no code implementations16 Jul 2020 Teng Liu, Wenhao Tan, Xiaolin Tang, Jinwei Zhang, Yang Xing, Dongpu Cao

This paper focusing on helping the relevant researchers realize the state-of-the-art of HEVs energy management field and also recognize its future development direction.

energy management Management

EM-GAN: Fast Stress Analysis for Multi-Segment Interconnect Using Generative Adversarial Networks

no code implementations27 Apr 2020 Wentian Jin, Sheriff Sadiqbatcha, Jinwei Zhang, Sheldon X. -D. Tan

Based on this observation, we train conditional GAN model using the images of many self-generated multi-segment wires and wire current densities and aging time (as conditions) against the COMSOL simulation results.

Image Generation

Inverse Reinforce Learning with Nonparametric Behavior Clustering

no code implementations15 Dec 2017 Siddharthan Rajasekaran, Jinwei Zhang, Jie Fu

In this paper, we introduce the Non-parametric Behavior Clustering IRL algorithm to simultaneously cluster demonstrations and learn multiple reward functions from demonstrations that may be generated from more than one behaviors.

Clustering

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