no code implementations • 26 Nov 2024 • Ben Philps, Maria del C. Valdes Hernandez, Chen Qin, Una Clancy, Eleni Sakka, Susana Munoz Maniega, Mark E. Bastin, Angela C. C. Jochems, Joanna M. Wardlaw, Miguel O. Bernabeu, Alzheimers Disease Neuroimaging Initiative
We demonstrate the downstream utility of UQ, proposing a novel method for classification of the clinical Fazekas score using spatial features extracted for WMH segmentation and UQ maps.
no code implementations • 22 Sep 2024 • Anurag Malyala, Zhenlin Zhang, Chengyan Wang, Chen Qin
Magnetic Resonance Imaging (MRI) is a powerful, non-invasive diagnostic tool; however, its clinical applicability is constrained by prolonged acquisition times.
no code implementations • 6 Aug 2024 • Shaoming Zheng, Yinsong Wang, Siyi Du, Chen Qin
Magnetic Resonance Imaging (MRI) is a leading diagnostic modality for a wide range of exams, where multiple contrast images are often acquired for characterizing different tissues.
1 code implementation • 10 Jul 2024 • Siyi Du, Shaoming Zheng, Yinsong Wang, Wenjia Bai, Declan P. O'Regan, Chen Qin
Moreover, TIP proposes a versatile tabular encoder tailored for incomplete, heterogeneous tabular data and a multimodal interaction module for inter-modality representation learning.
1 code implementation • 27 Jun 2024 • Zi Wang, Fanwen Wang, Chen Qin, Jun Lyu, Cheng Ouyang, Shuo Wang, Yan Li, Mengyao Yu, Haoyu Zhang, Kunyuan Guo, Zhang Shi, Qirong Li, Ziqiang Xu, Yajing Zhang, Hao Li, Sha Hua, Binghua Chen, Longyu Sun, Mengting Sun, Qin Li, Ying-Hua Chu, Wenjia Bai, Jing Qin, Xiahai Zhuang, Claudia Prieto, Alistair Young, Michael Markl, He Wang, Lianming Wu, Guang Yang, Xiaobo Qu, Chengyan Wang
To the best of our knowledge, the CMRxRecon2024 dataset is the largest and most protocal-diverse publicly available cardiac k-space dataset.
no code implementations • 2 Apr 2024 • Yifan Wu, Mengjin Dong, Rohit Jena, Chen Qin, James C. Gee
Leveraging Neural Ordinary Differential Equations (ODE) for registration, this extension work discusses how this framework can aid in the characterization of sequential biological processes.
1 code implementation • 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.
1 code implementation • 12 Feb 2024 • Yuyang Xue, Chen Qin, Sotirios A. Tsaftaris
In this work, by employing a conditional hyperparameter network, we eliminate the need of augmentation, yet maintain robust performance under various levels of Gaussian noise.
no code implementations • 10 Dec 2023 • Hollan Haule, Ian Piper, Patricia Jones, Chen Qin, Tsz-Yan Milly Lo, Javier Escudero
We show that our unsupervised approach achieves comparable sensitivity to fully supervised methods and generalizes well to an external dataset.
2 code implementations • 19 Sep 2023 • Chengyan Wang, Jun Lyu, Shuo Wang, Chen Qin, Kunyuan Guo, Xinyu Zhang, Xiaotong Yu, Yan Li, Fanwen Wang, Jianhua Jin, Zhang Shi, Ziqiang Xu, Yapeng Tian, Sha Hua, Zhensen Chen, Meng Liu, Mengting Sun, Xutong Kuang, Kang Wang, Haoran Wang, Hao Li, Yinghua Chu, Guang Yang, Wenjia Bai, Xiahai Zhuang, He Wang, Jing Qin, Xiaobo Qu
However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images.
1 code implementation • 30 May 2023 • Zeju Li, Konstantinos Kamnitsas, Qi Dou, Chen Qin, Ben Glocker
We demonstrate the effectiveness of our method on four medical image segmentation tasks across different scenarios with two state-of-the-art segmentation models, DeepMedic and nnU-Net.
no code implementations • 19 Mar 2023 • Yinsong Wang, Huaqi Qiu, Chen Qin
The proposed method introduces a spatially-variant regularization and learns its effect of achieving spatially-adaptive regularization by conditioning the registration network on the hyperparameter matrix via CSAIN.
1 code implementation • 12 Oct 2022 • Shuo Wang, Chen Qin, Chengyan Wang, Kang Wang, Haoran Wang, Chen Chen, Cheng Ouyang, Xutong Kuang, Chengliang Dai, Yuanhan Mo, Zhang Shi, Chenchen Dai, Xinrong Chen, He Wang, Wenjia Bai
The quality of cardiac magnetic resonance (CMR) imaging is susceptible to respiratory motion artifacts.
no code implementations • 21 Sep 2022 • Chen Qin, Daniel Rueckert
Artificial intelligence (AI) and Machine Learning (ML) have shown great potential in improving the medical imaging workflow, from image acquisition and reconstruction to disease diagnosis and treatment.
no code implementations • 29 Jul 2022 • Qingjie Meng, Chen Qin, Wenjia Bai, Tianrui Liu, Antonio de Marvao, Declan P O'Regan, Daniel Rueckert
To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart.
1 code implementation • 8 Jun 2022 • Chen Qin, Shuo Wang, Chen Chen, Wenjia Bai, Daniel Rueckert
In contrast to most existing approaches which impose explicit generic regularization such as smoothness, in this work we propose a novel method that can implicitly learn an application-specific biomechanics-informed prior and embed it into a neural network-parameterized transformation model.
no code implementations • 15 Mar 2022 • Tian Xia, Pedro Sanchez, Chen Qin, Sotirios A. Tsaftaris
To demonstrate the effectiveness of the proposed approach, we validate the method with the classification of Alzheimer's Disease (AD) as a downstream task.
no code implementations • 11 Mar 2022 • Hollan Haule, Evangelos Kafantaris, Tsz-Yan Milly Lo, Chen Qin, Javier Escudero
Manual annotation by experienced researchers, which is the gold standard for removing artifacts, is time-consuming and costly due to the volume of the data generated in the ICU.
1 code implementation • 7 Dec 2021 • Huaqi Qiu, Kerstin Hammernik, Chen Qin, Chen Chen, Daniel Rueckert
Deep learning (DL) image registration methods amortize the costly pair-wise iterative optimization by training deep neural networks to predict the optimal transformation in one fast forward-pass.
1 code implementation • 24 Nov 2021 • Cheng Ouyang, Chen Chen, Surui Li, Zeju Li, Chen Qin, Wenjia Bai, Daniel Rueckert
In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data is only available from one source domain, which is common in medical imaging applications.
1 code implementation • ICCV 2021 • Shuang Li, Mixue Xie, Fangrui Lv, Chi Harold Liu, Jian Liang, Chen Qin, Wei Li
To tackle this issue, we propose Semantic Concentration for Domain Adaptation (SCDA), which encourages the model to concentrate on the most principal features via the pair-wise adversarial alignment of prediction distributions.
1 code implementation • 7 Aug 2021 • Chen Chen, Chen Qin, Cheng Ouyang, Zeju Li, Shuo Wang, Huaqi Qiu, Liang Chen, Giacomo Tarroni, Wenjia Bai, Daniel Rueckert
The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training.
no code implementations • 8 Jul 2021 • Shuo Wang, Chen Qin, Nicolo Savioli, Chen Chen, Declan O'Regan, Stuart Cook, Yike Guo, Daniel Rueckert, Wenjia Bai
In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures.
2 code implementations • 2 Jul 2021 • Chen Chen, Kerstin Hammernik, Cheng Ouyang, Chen Qin, Wenjia Bai, Daniel Rueckert
In this paper, we present a cooperative framework for training image segmentation models and a latent space augmentation method for generating hard examples.
1 code implementation • 22 Dec 2020 • Chen Qin, Jinming Duan, Kerstin Hammernik, Jo Schlemper, Thomas Küstner, René Botnar, Claudia Prieto, Anthony N. Price, Joseph V. Hajnal, Daniel Rueckert
The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatio-temporal redundancies in complementary domains.
1 code implementation • 13 Dec 2020 • Shuang Li, Fangrui Lv, Binhui Xie, Chi Harold Liu, Jian Liang, Chen Qin
Motivated by the observation that target samples cannot always be separated distinctly by the decision boundary, here in the proposed BCDM, we design a novel classifier determinacy disparity (CDD) metric, which formulates classifier discrepancy as the class relevance of distinct target predictions and implicitly introduces constraint on the target feature discriminability.
no code implementations • 12 Jul 2020 • Chen Qin, Jo Schlemper, Kerstin Hammernik, Jinming Duan, Ronald M. Summers, Daniel Rueckert
We present a deep network interpolation strategy for accelerated parallel MR image reconstruction.
no code implementations • 23 Jun 2020 • Shuo Wang, Giacomo Tarroni, Chen Qin, Yuanhan Mo, Chengliang Dai, Chen Chen, Ben Glocker, Yike Guo, Daniel Rueckert, Wenjia Bai
Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows.
1 code implementation • 23 Jun 2020 • Chen Chen, Chen Qin, Huaqi Qiu, Cheng Ouyang, Shuo Wang, Liang Chen, Giacomo Tarroni, Wenjia Bai, Daniel Rueckert
In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation.
1 code implementation • 8 Jun 2020 • Chen Qin, Shuo Wang, Chen Chen, Huaqi Qiu, Wenjia Bai, Daniel Rueckert
The learnt VAE regulariser then can be coupled with any deep learning based registration network to regularise the solution space to be biomechanically plausible.
1 code implementation • 18 Dec 2019 • Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, Daniel Rueckert
Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a $\Sigma$-net, for accelerated parallel MR image reconstruction using deep learning.
1 code implementation • 11 Dec 2019 • Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, Kerstin Hammernik
We explore an ensembled $\Sigma$-net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit sensitivity maps.
no code implementations • 9 Nov 2019 • Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
Deep learning has become the most widely used approach for cardiac image segmentation in recent years.
no code implementations • 25 Sep 2019 • Jo Schlemper, Jinming Duan, Cheng Ouyang, Chen Qin, Jose Caballero, Joseph V. Hajnal, Daniel Rueckert
We show that the proposed approaches are competitive relative to the state of the art both quantitatively and qualitatively.
no code implementations • 20 Aug 2019 • Chen Qin, Wenjia Bai, Jo Schlemper, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Daniel Rueckert
Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data.
1 code implementation • 22 Jul 2019 • Chen Qin, Jo Schlemper, Jinming Duan, Gavin Seegoolam, Anthony Price, Joseph Hajnal, Daniel Rueckert
Experiments conducted on highly undersampled short-axis cardiac cine MRI scans demonstrate that our proposed method outperforms the current state-of-the-art dynamic MR reconstruction approaches both quantitatively and qualitatively.
1 code implementation • 19 Jul 2019 • Jinming Duan, Jo Schlemper, Chen Qin, Cheng Ouyang, Wenjia Bai, Carlo Biffi, Ghalib Bello, Ben Statton, Declan P. O'Regan, Daniel Rueckert
In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data.
no code implementations • 22 Mar 2019 • Chen Qin, Bibo Shi, Rui Liao, Tommaso Mansi, Daniel Rueckert, Ali Kamen
The proposed registration approach is then built on the factorized latent shape code, with the assumption that the intrinsic shape deformation existing in original image domain is preserved in this latent space.
no code implementations • 1 Aug 2018 • Wenjia Bai, Hideaki Suzuki, Chen Qin, Giacomo Tarroni, Ozan Oktay, Paul M. Matthews, Daniel Rueckert
In this work, we propose an image sequence segmentation algorithm by combining a fully convolutional network with a recurrent neural network, which incorporates both spatial and temporal information into the segmentation task.
1 code implementation • 11 Jun 2018 • Chen Qin, Wenjia Bai, Jo Schlemper, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Daniel Rueckert
Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases.
4 code implementations • 5 Dec 2017 • Chen Qin, Jo Schlemper, Jose Caballero, Anthony Price, Joseph V. Hajnal, Daniel Rueckert
In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modelling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations.