1 code implementation • 3 Apr 2024 • Yunjie Chen, Jelmer M. Wolterink, Olaf M. Neve, Stephan R. Romeijn, Berit M. Verbist, Erik F. Hensen, Qian Tao, Marius Staring
In the proposed method, each tumor is represented as a signed distance function (SDF) conditioned on a low-dimensional latent code.
no code implementations • 1 Apr 2024 • Jun Lyu, Chen Qin, Shuo Wang, Fanwen Wang, Yan Li, Zi Wang, Kunyuan Guo, Cheng Ouyang, Michael Tänzer, Meng Liu, Longyu Sun, Mengting Sun, Qin Li, Zhang Shi, Sha Hua, Hao Li, Zhensen Chen, Zhenlin Zhang, Bingyu Xin, Dimitris N. Metaxas, George Yiasemis, Jonas Teuwen, Liping Zhang, Weitian Chen, Yidong Zhao, Qian Tao, Yanwei Pang, Xiaohan Liu, Artem Razumov, Dmitry V. Dylov, Quan Dou, Kang Yan, Yuyang Xue, Yuning Du, Julia Dietlmeier, Carles Garcia-Cabrera, Ziad Al-Haj Hemidi, Nora Vogt, Ziqiang Xu, Yajing Zhang, Ying-Hua Chu, Weibo Chen, Wenjia Bai, Xiahai Zhuang, Jing Qin, Lianmin Wu, Guang Yang, Xiaobo Qu, He Wang, Chengyan Wang
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
no code implementations • 4 Mar 2024 • Yidong Zhao, Joao Tourais, Iain Pierce, Christian Nitsche, Thomas A. Treibel, Sebastian Weingärtner, Artur M. Schweidtmann, Qian Tao
Deep learning (DL)-based methods have achieved state-of-the-art performance for a wide range of medical image segmentation tasks.
no code implementations • 1 Mar 2024 • Yidong Zhao, Yi Zhang, Qian Tao
Deep learning-based methods have achieved prestigious performance for magnetic resonance imaging (MRI) reconstruction, enabling fast imaging for many clinical applications.
no code implementations • 3 Nov 2023 • Xinqi Li, Yi Zhang, Yidong Zhao, Jan van Gemert, Qian Tao
To address the challenge, we propose a novel motion correction framework based on robust principle component analysis (rPCA) that decomposes quantitative cardiac MRI into low-rank and sparse components, and we integrate the groupwise CNN-based registration backbone within the rPCA framework.
1 code implementation • 6 Sep 2023 • Yunjie Chen, Marius Staring, Olaf M. Neve, Stephan R. Romeijn, Erik F. Hensen, Berit M. Verbist, Jelmer M. Wolterink, Qian Tao
In this paper, we propose Conditional Neural fields with Shift modulation (CoNeS), a model that takes voxel coordinates as input and learns a representation of the target images for multi-sequence MRI translation.
no code implementations • 7 Jun 2023 • Jianpeng Liao, Jun Yan, Qian Tao
The DualHGNN first leverages a multi-view hypergraph learning network to explore the optimal hypergraph structure from multiple views, constrained by a consistency loss proposed to improve its generalization.
1 code implementation • 24 Mar 2023 • Qian Tao, Zhen Wang, Wenyuan Yu, Yaliang Li, Zhewei Wei
In recent years, a plethora of spectral graph neural networks (GNN) methods have utilized polynomial basis with learnable coefficients to achieve top-tier performances on many node-level tasks.
no code implementations • 2 Feb 2023 • Yunjie Chen, Marius Staring, Jelmer M. Wolterink, Qian Tao
In this paper, we propose a novel MR image translation solution based on local implicit neural representations.
no code implementations • 12 Dec 2022 • Yidong Zhao, Changchun Yang, Artur Schweidtmann, Qian Tao
Different from previous baseline methods such as Monte Carlo Dropout and mean-field Bayesian Neural Networks, our proposed method does not require a variational architecture and keeps the original nnU-Net architecture intact, thereby preserving its excellent performance and ease of use.
no code implementations • 27 Jan 2022 • Jianpeng Liao, Qian Tao, Jun Yan
Graph-based semi-supervised learning, which can exploit the connectivity relationship between labeled and unlabeled data, has been shown to outperform the state-of-the-art in many artificial intelligence applications.
1 code implementation • 31 Oct 2021 • Zixia Zhou, Xinrui Zu, Yuanyuan Wang, Boudewijn P. F. Lelieveldt, Qian Tao
Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value.
no code implementations • 29 Sep 2021 • Xinrui Zu, Qian Tao
Dimensionality reduction (DR) and visualization of high-dimensional data is of theoretical and practical value in machine learning and related fields.
no code implementations • 12 Apr 2021 • Zixia Zhou, Yuanyuan Wang, Boudewijn P. F. Lelieveldt, Qian Tao
t-distributed stochastic neighbor embedding (t-SNE) is a well-established visualization method for complex high-dimensional data.
1 code implementation • 26 Apr 2020 • Zhaohan Xiong, Qing Xia, Zhiqiang Hu, Ning Huang, Cheng Bian, Yefeng Zheng, Sulaiman Vesal, Nishant Ravikumar, Andreas Maier, Xin Yang, Pheng-Ann Heng, Dong Ni, Caizi Li, Qianqian Tong, Weixin Si, Elodie Puybareau, Younes Khoudli, Thierry Geraud, Chen Chen, Wenjia Bai, Daniel Rueckert, Lingchao Xu, Xiahai Zhuang, Xinzhe Luo, Shuman Jia, Maxime Sermesant, Yashu Liu, Kuanquan Wang, Davide Borra, Alessandro Masci, Cristiana Corsi, Coen de Vente, Mitko Veta, Rashed Karim, Chandrakanth Jayachandran Preetha, Sandy Engelhardt, Menyun Qiao, Yuanyuan Wang, Qian Tao, Marta Nunez-Garcia, Oscar Camara, Nicolo Savioli, Pablo Lamata, Jichao Zhao
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
no code implementations • 30 Oct 2019 • Wenjun Yan, Yuanyuan Wang, Shengjia Gu, Lu Huang, Fuhua Yan, Liming Xia, Qian Tao
In this work, we proposed a generic framework to address this problem, consisting of (1) an unpaired generative adversarial network (GAN) for vendor-adaptation, and (2) a Unet for object segmentation.
no code implementations • 20 Oct 2018 • Wenjun Yan, Yuanyuan Wang, Zeju Li, Rob J. van der Geest, Qian Tao
Quantitative assessment of left ventricle (LV) function from cine MRI has significant diagnostic and prognostic value for cardiovascular disease patients.