Search Results for author: Wenqi Huang

Found 18 papers, 8 papers with code

Direct Cardiac Segmentation from Undersampled K-space Using Transformers

1 code implementation31 May 2024 Yundi Zhang, Nil Stolt-Ansó, Jiazhen Pan, Wenqi Huang, Kerstin Hammernik, Daniel Rueckert

The prevailing deep learning-based methods of predicting cardiac segmentation involve reconstructed magnetic resonance (MR) images.

Cardiac Segmentation Segmentation

Propagation and Attribution of Uncertainty in Medical Imaging Pipelines

1 code implementation28 Sep 2023 Leonhard F. Feiner, Martin J. Menten, Kerstin Hammernik, Paul Hager, Wenqi Huang, Daniel Rueckert, Rickmer F. Braren, Georgios Kaissis

In this paper, we propose a method to propagate uncertainty through cascades of deep learning models in medical imaging pipelines.

ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space

1 code implementation17 Aug 2023 Veronika Spieker, Wenqi Huang, Hannah Eichhorn, Jonathan Stelter, Kilian Weiss, Veronika A. Zimmer, Rickmer F. Braren, Dimitrios C. Karampinos, Kerstin Hammernik, Julia A. Schnabel

Motion-resolved reconstruction for abdominal magnetic resonance imaging (MRI) remains a challenge due to the trade-off between residual motion blurring caused by discretized motion states and undersampling artefacts.

Propheter: Prophetic Teacher Guided Long-Tailed Distribution Learning

1 code implementation9 Apr 2023 Wenxiang Xu, Yongcheng Jing, Linyun Zhou, Wenqi Huang, Lechao Cheng, Zunlei Feng, Mingli Song

This is specifically achieved by devising an elaborated ``prophetic'' teacher, termed as ``Propheter'', that aims to learn the potential class distributions.

Data Augmentation

Reconstruction-driven motion estimation for motion-compensated MR CINE imaging

no code implementations5 Feb 2023 Jiazhen Pan, Wenqi Huang, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik

Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem into two sub-optimization problems, i. e. motion estimation and reconstruction, we formulate this problem as a single entity with one single optimization.

Motion Estimation

Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imaging

no code implementations16 Dec 2022 Wenqi Huang, Hongwei Li, Jiazhen Pan, Gastao Cruz, Daniel Rueckert, Kerstin Hammernik

While existing methods bin acquired data from neighboring time points to reconstruct one phase of the cardiac motion, our framework allows for a continuous, binning-free, and subject-specific k-space representation. We assign a unique coordinate that consists of time, coil index, and frequency domain location to each sampled k-space point.

Image Reconstruction

Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks

1 code implementation ICCV 2023 Qihan Huang, Mengqi Xue, Wenqi Huang, Haofei Zhang, Jie Song, Yongcheng Jing, Mingli Song

Part-prototype networks (e. g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts.

Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control

no code implementations30 Mar 2022 Qiong Liu, Ye Guo, Lirong Deng, Haotian Liu, Dongyu Li, Hongbin Sun, Wenqi Huang

Then we design the one-step actor-critic DRL scheme which is a simplified version of recent DRL algorithms, and it avoids the issue of Q value overestimation successfully.

Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI

no code implementations18 Dec 2021 Zhuo-Xu Cui, Jing Cheng, Qingyong Zhu, Yuanyuan Liu, Sen Jia, Kankan Zhao, Ziwen Ke, Wenqi Huang, Haifeng Wang, Yanjie Zhu, Dong Liang

Specifically, focusing on accelerated MRI, we unroll a zeroth-order algorithm, of which the network module represents the regularizer itself, so that the network output can be still covered by the regularization model.

MRI Reconstruction Rolling Shutter Correction

A Multivariate Density Forecast Approach for Online Power System Security Assessment

no code implementations7 May 2021 Zichao Meng, Ye Guo, Wenjun Tang, Hongbin Sun, Wenqi Huang

A multivariate density forecast model based on deep learning is designed in this paper to forecast the joint cumulative distribution functions (JCDFs) of multiple security margins in power systems.

Deep Low-rank plus Sparse Network for Dynamic MR Imaging

1 code implementation26 Oct 2020 Wenqi Huang, Ziwen Ke, Zhuo-Xu Cui, Jing Cheng, Zhilang Qiu, Sen Jia, Leslie Ying, Yanjie Zhu, Dong Liang

However, the selection of the parameters of L+S is empirical, and the acceleration rate is limited, which are common failings of iterative compressed sensing MR imaging (CS-MRI) reconstruction methods.

MRI Reconstruction

Fusion Based Holistic Road Scene Understanding

no code implementations29 Jun 2014 Wenqi Huang, Xiaojin Gong

This paper addresses the problem of holistic road scene understanding based on the integration of visual and range data.

Clustering Image Segmentation +4

Iterated Tabu Search Algorithm for Packing Unequal Circles in a Circle

no code implementations4 Jun 2013 Tao Ye, Wenqi Huang, Zhipeng Lu

Meanwhile, it builds a neighborhood structure on the set of local minimum via two appropriate perturbation moves and integrates two combinatorial optimization methods, Tabu Search and Iterated Local Search, to systematically search for good local minima.

Combinatorial Optimization

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