Jinming Duan

Jinming Duan
ORCID: 0000-0002-5108-2128

Colleges, School and Institutes

Research interests

Image Segmentation and Tracking, Image Reconstruction, Medical Image Computing, Machine Learning, Deep Learning, Neural Networks

Education/Academic qualification

  • Doctor of Engineering, University of Nottingham

    Variational and PDE-based methods for image processing

    1 Oct 2014 - 30 Sep 2017

Professional Qualifications

  • Imperial College London, Research Associate

    18 Sep 2017 - 18 Mar 2019

Willingness to take PhD students

Yes

PhD projects

I am interested in deep neural networks, variational methods, partial/ordinary differential equations, numerical optimisation, and finite difference/element methods, with applications to image processing, computer vision and medical imaging analysis.

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Most downloaded publications

Cardiac MR segmentation from undersampled k-space using deep latent representation learning
Jinming Duan, 26 Sep 2018, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I. Schnabel, J. A., Davatzikos, C., Alberola-López, C., Fichtinger, G. & Frangi, A. F. (eds.). Springer Verlag, p. 259-267 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11070 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

154 downloads
Combining deep learning and shape priors for bi-ventricular segmentation of volumetric cardiac magnetic resonance images
Jinming Duan, 23 Nov 2018, Shape in Medical Imaging : nternational Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings. Lombaert, H., Paniagua, B., Egger, B., Lüthi, M., Reuter, M. & Wachinger, C. (eds.). Springer Verlag, p. 258-267 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11167 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

149 downloads
Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography
Wenqi Lu, Jinming Duan & Iain Styles, 2 May 2019, In: Biomedical Optics Express. 10, 6, p. 2684-2707

Research output: Contribution to journal › Article › peer-review

140 downloads
A portable image-based cytometer for rapid malaria detection and quantification
Jinming Duan, 8 Jun 2017, In: PLoS ONE. 12, 6, 18 p., e0179161.

Research output: Contribution to journal › Article › peer-review

123 downloads
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
Jinming Duan, Sep 2019, In: IEEE Transactions on Medical Imaging. 38, 9, p. 2151-2164 14 p.

Research output: Contribution to journal › Article › peer-review

119 downloads

Recent publications

Explainable anatomical shape analysis through deep hierarchical generative models
Jinming Duan, Jun 2020, In: IEEE Transactions on Medical Imaging. 39, 6, p. 2088-2099 12 p.

Research output: Contribution to journal › Article › peer-review

PointPoseNet:Point pose network for robust 6D object pose estimation
Jinming Duan, Hector Basevi, Hyung Jin Chang & Ales Leonardis, Mar 2020, Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020. Institute of Electrical and Electronics Engineers (IEEE), p. 2813-2822 10 p. 9093272. (Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

G2L-Net:Global to local network for real-time 6D pose estimation with embedding vector features
Hyung Jin Chang, Jinming Duan, 2020, In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. p. 4232-4241 10 p., 9157169.

Research output: Contribution to journal › Conference article › peer-review

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