Search Results for author: Chen Qin

Found 34 papers, 19 papers with code

Inference Stage Denoising for Undersampled MRI Reconstruction

1 code implementation12 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.

Data Augmentation Denoising +1

VAE-IF: Deep feature extraction with averaging for unsupervised artifact detection in routine acquired ICU time-series

no code implementations10 Dec 2023 Hollan Haule, Ian Piper, Patricia Jones, Chen Qin, Tsz-Yan Milly Lo, Javier Escudero

Our approach offers a promising solution for cleaning ICU data in clinical research and practice without the need for any labels whatsoever.

Time Series

Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation

1 code implementation30 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.

Data Augmentation Image Segmentation +5

Conditional Deformable Image Registration with Spatially-Variant and Adaptive Regularization

no code implementations19 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.

Hyperparameter Optimization Image Registration

Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic Resonance

no code implementations21 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.

Computational Efficiency Image Reconstruction

MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI

no code implementations29 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.

Motion Estimation

Generative Myocardial Motion Tracking via Latent Space Exploration with Biomechanics-informed Prior

1 code implementation8 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.

Image Registration

Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification

no code implementations15 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.

Classification counterfactual +1

Hybrid Artifact Detection System for Minute Resolution Blood Pressure Signals from ICU

no code implementations11 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.

Decision Making Specificity

Embedding Gradient-based Optimization in Image Registration Networks

1 code implementation7 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.

Image Reconstruction Image Registration

Causality-inspired Single-source Domain Generalization for Medical Image Segmentation

1 code implementation24 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.

Data Augmentation Domain Generalization +4

Semantic Concentration for Domain Adaptation

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.

Domain Adaptation Transfer Learning

Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation

no code implementations8 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.

Anatomy Cardiac Segmentation +2

Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation

2 code implementations2 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.

Data Augmentation Image Reconstruction +4

Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction

1 code implementation22 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.

De-aliasing Image Reconstruction

Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation

1 code implementation13 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.

Semantic Segmentation

Deep Generative Model-based Quality Control for Cardiac MRI Segmentation

no code implementations23 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.

Image Segmentation MRI segmentation +2

Realistic Adversarial Data Augmentation for MR Image Segmentation

1 code implementation23 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.

Data Augmentation Image Segmentation +3

Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI

1 code implementation8 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.

Image Registration

$Σ$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction

1 code implementation18 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.

Image Enhancement Image Reconstruction +1

$Σ$-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction

1 code implementation11 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.

Image Reconstruction SSIM +1

Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image

no code implementations20 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.

Image Reconstruction Motion Estimation +1

k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations

1 code implementation22 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.

Image Reconstruction

VS-Net: Variable splitting network for accelerated parallel MRI reconstruction

1 code implementation19 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.

MRI Reconstruction Rolling Shutter Correction

Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations

no code implementations22 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.

Image Registration Image-to-Image Translation

Recurrent neural networks for aortic image sequence segmentation with sparse annotations

no code implementations1 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.

Anatomy Segmentation

Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

1 code implementation11 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.

Cardiac Segmentation Motion Estimation +2

Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction

4 code implementations5 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.

Image Reconstruction Temporal Sequences

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