no code implementations • 22 Mar 2023 • Tiantian Geng, Teng Wang, Jinming Duan, Runmin Cong, Feng Zheng
To better adapt to real-life applications, in this paper we focus on the task of dense-localizing audio-visual events, which aims to jointly localize and recognize all audio-visual events occurring in an untrimmed video.
no code implementations • 9 Dec 2022 • Wei Chen, Xi Jia, Zhongqun Zhang, Hyung Jin Chang, Linlin Shen, Jinming Duan, Ales Leonardis
The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task.
1 code implementation • 29 Nov 2022 • Xi Jia, Joseph Bartlett, Wei Chen, Siyang Song, Tianyang Zhang, Xinxing Cheng, Wenqi Lu, Zhaowen Qiu, Jinming Duan
Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain.
1 code implementation • 7 Aug 2022 • Xi Jia, Joseph Bartlett, Tianyang Zhang, Wenqi Lu, Zhaowen Qiu, Jinming Duan
On the public 3D IXI brain dataset for atlas-based registration, we show that the performance of the vanilla U-Net is already comparable with that of state-of-the-art transformer-based networks (such as TransMorph), and that the proposed LKU-Net outperforms TransMorph by using only 1. 12% of its parameters and 10. 8% of its mult-adds operations.
no code implementations • 25 May 2022 • Tianyang Zhang, Shaoming Zheng, Jun Cheng, Xi Jia, Joseph Bartlett, Xinxing Cheng, Huazhu Fu, Zhaowen Qiu, Jiang Liu, Jinming Duan
It consists of a spatial transformation block followed by an intensity distribution rendering module.
no code implementations • 8 Dec 2021 • Alessa Hering, Lasse Hansen, Tony C. W. Mok, Albert C. S. Chung, Hanna Siebert, Stephanie Häger, Annkristin Lange, Sven Kuckertz, Stefan Heldmann, Wei Shao, Sulaiman Vesal, Mirabela Rusu, Geoffrey Sonn, Théo Estienne, Maria Vakalopoulou, Luyi Han, Yunzhi Huang, Pew-Thian Yap, Mikael Brudfors, Yaël Balbastre, Samuel Joutard, Marc Modat, Gal Lifshitz, Dan Raviv, Jinxin Lv, Qiang Li, Vincent Jaouen, Dimitris Visvikis, Constance Fourcade, Mathieu Rubeaux, Wentao Pan, Zhe Xu, Bailiang Jian, Francesca De Benetti, Marek Wodzinski, Niklas Gunnarsson, Jens Sjölund, Daniel Grzech, Huaqi Qiu, Zeju Li, Alexander Thorley, Jinming Duan, Christoph Großbröhmer, Andrew Hoopes, Ingerid Reinertsen, Yiming Xiao, Bennett Landman, Yuankai Huo, Keelin Murphy, Nikolas Lessmann, Bram van Ginneken, Adrian V. Dalca, Mattias P. Heinrich
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
1 code implementation • 6 Oct 2021 • Joseph Bartlett, Jinming Duan
To overcome these drawbacks, we propose a novel adaption to Total Generalised Variation (TGV) regularisation called Total Smooth Variation (TSV), which retains edges and meanwhile does not produce results which contain staircase artefacts.
no code implementations • 26 Sep 2021 • Alexander Thorley, Xi Jia, Hyung Jin Chang, Boyang Liu, Karina Bunting, Victoria Stoll, Antonio de Marvao, Declan P. O'Regan, Georgios Gkoutos, Dipak Kotecha, Jinming Duan
Recent developments in stochastic approaches based on deep learning have achieved sub-second runtimes for DiffIR with competitive registration accuracy, offering a fast alternative to conventional iterative methods.
no code implementations • 25 May 2021 • Xi Jia, Alexander Thorley, Wei Chen, Huaqi Qiu, Linlin Shen, Iain B Styles, Hyung Jin Chang, Ales Leonardis, Antonio de Marvao, Declan P. O'Regan, Daniel Rueckert, Jinming Duan
We then propose two neural layers (i. e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i. e. generalized denoising layer).
2 code implementations • CVPR 2021 • Wei Chen, Xi Jia, Hyung Jin Chang, Jinming Duan, Linlin Shen, Ales Leonardis
In this paper, we focus on category-level 6D pose and size estimation from monocular RGB-D image.
Ranked #6 on 6D Pose Estimation using RGBD on REAL275
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 • ECCV 2020 • Weizeng Lu, Xi Jia, Weicheng Xie, Linlin Shen, Yicong Zhou, Jinming Duan
The detector predicts the object location defined by a set of coefficients describing a geometric shape (i. e. ellipse or rectangle), which is geometrically constrained by the mask produced by the generator.
Weakly Supervised Object Localization Weakly-Supervised Object Localization
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.
1 code implementation • CVPR 2020 • Wei Chen, Xi Jia, Hyung Jin Chang, Jinming Duan, Ales Leonardis
Third, via the predicted segmentation and translation, we transfer the fine object point cloud into a local canonical coordinate, in which we train a rotation localization network to estimate initial object rotation.
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.
1 code implementation • 24 Sep 2019 • Jo Schlemper, Ilkay Oksuz, James R. Clough, Jinming Duan, Andrew P. King, Julia A. Schnabel, Joseph V. Hajnal, Daniel Rueckert
AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited.
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 • 5 Jul 2019 • Wenjia Bai, Chen Chen, Giacomo Tarroni, Jinming Duan, Florian Guitton, Steffen E. Petersen, Yike Guo, Paul M. Matthews, Daniel Rueckert
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks.
no code implementations • 5 Jul 2019 • Cheng Ouyang, Konstantinos Kamnitsas, Carlo Biffi, Jinming Duan, Daniel Rueckert
Deep unsupervised domain adaptation (UDA) aims to improve the performance of a deep neural network model on a target domain, using solely unlabelled target domain data and labelled source domain data.
1 code implementation • 28 Jun 2019 • Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni, Wenjia Bai, Antonio de Marvao, Ozan Oktay, Christian Ledig, Loic Le Folgoc, Konstantinos Kamnitsas, Georgia Doumou, Jinming Duan, Sanjay K. Prasad, Stuart A. Cook, Declan P. O'Regan, Daniel Rueckert
At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space.
no code implementations • 3 Jun 2019 • Wenqi Lu, Jinming Duan, Joshua Deepak Veesa, Iain B. Styles
The forward model in diffuse optical tomography (DOT) describes how light propagates through a turbid medium.
no code implementations • 7 Jan 2019 • Wenqi Lu, Jinming Duan, David Orive-Miguel, Lionel Herve, Iain B. Styles
Total variation (TV) is a powerful regularization method that has been widely applied in different imaging applications, but is difficult to apply to diffuse optical tomography (DOT) image reconstruction (inverse problem) due to complex and unstructured geometries, non-linearity of the data fitting and regularization terms, and non-differentiability of the regularization term.
1 code implementation • 8 Oct 2018 • Ghalib A. Bello, Timothy J. W. Dawes, Jinming Duan, Carlo Biffi, Antonio de Marvao, Luke S. G. E. Howard, J. Simon R. Gibbs, Martin R. Wilkins, Stuart A. Cook, Daniel Rueckert, Declan P. O'Regan
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images.
1 code implementation • 26 Aug 2018 • Jinming Duan, Ghalib Bello, Jo Schlemper, Wenjia Bai, Timothy J. W. Dawes, Carlo Biffi, Antonio de Marvao, Georgia Doumou, Declan P. O'Regan, Daniel Rueckert
The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation.
no code implementations • 8 Aug 2018 • Jinming Duan, Weicheng Xie, Ryan Wen Liu, Christopher Tench, Irene Gottlob, Frank Proudlock, Li Bai
The retinal layer boundary model consists of 9 open parametric contours representing the 9 retinal layers in OCT images.
no code implementations • 27 Jul 2018 • Jinming Duan, Jo Schlemper, Wenjia Bai, Timothy J. W. Dawes, Ghalib Bello, Georgia Doumou, Antonio de Marvao, Declan P. O'Regan, Daniel Rueckert
In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH).
no code implementations • 27 Sep 2016 • Jinming Duan, Wil OC Ward, Luke Sibbett, Zhenkuan Pan, Li Bai
Second order total variation (SOTV) models have advantages for image reconstruction over their first order counterparts including their ability to remove the staircase artefact in the reconstructed image, but they tend to blur the reconstructed image.
no code implementations • 7 Sep 2016 • Jinming Duan, Christopher Tench, Irene Gottlob, Frank Proudlock, Li Bai
OCT image segmentation to localise retinal layer boundaries is a fundamental procedure for diagnosing and monitoring the progression of retinal and optical nerve disorders.