Staff profile

I am an Assistant Professor in the Department of Computer Science at Durham University. My research interests are computational and mathematical intelligence, non-linear analysis and computing, and the applications in biomedical and physical sciences. I am actively seeking collaborative opportunities motivated by industrial practices and theoretical principles across disciplines.

• Visual Computing
• Machine Learning

Chapter in book

• ST-SACLF: Style Transfer Informed Self-Attention Classifier for Bias-Aware Painting Classification
  
  Vijendran, M., Li, F. W. B., Deng, J., & Shum, H. P. H. (in press). ST-SACLF: Style Transfer Informed Self-Attention Classifier for Bias-Aware Painting Classification. In CCIS ’24: Communications in Computer and Information Science. Springer.

Conference Paper

• Centersam: Fully Automatic Prompt for Dense Nucleus Segmentation
  
  Li, Y., Ren, H., Deng, J., Ma, X., & Xie, X. (2024). Centersam: Fully Automatic Prompt for Dense Nucleus Segmentation. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE. https://doi.org/10.1109/isbi56570.2024.10635872
• An Element-Wise Weights Aggregation Method for Federated Learning
  
  Hu, Y., Ren, H., Hu, C., Deng, J., & Xie, X. (2023). An Element-Wise Weights Aggregation Method for Federated Learning. In 2023 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE. https://doi.org/10.1109/icdmw60847.2023.00031
• MedZip: 3D medical images lossless compressor using recurrent neural network (LSTM)
  
  Nagoor, O. H., Whittle, J., Deng, J., Mora, B., & Jones, M. W. (2021). MedZip: 3D medical images lossless compressor using recurrent neural network (LSTM). Presented at 2020 25th International Conference on Pattern Recognition (ICPR) IEEE. https://doi.org/10.1109/icpr48806.2021.9413341
• Locating Datacenter Link Faults with a Directed Graph Convolutional Neural Network.
  
  Kenning, M. P., Deng, J., Edwards, M., & Xie, X. (2021). Locating Datacenter Link Faults with a Directed Graph Convolutional Neural Network. Presented at ICPRAM. https://doi.org/10.5220/0010301403120320
• Lossless compression for volumetric medical images using deep neural network with local sampling
  
  Nagoor, O. H., Whittle, J., Deng, J., Mora, B., & Jones, M. W. (2020). Lossless compression for volumetric medical images using deep neural network with local sampling. Presented at 2020 IEEE International Conference on Image Processing (ICIP) IEEE. https://doi.org/10.1109/icip40778.2020.9191031
• Learning discriminatory deep clustering models
  
  Alqahtani, A., Xie, X., Deng, J., & Jones, M. W. (2019). Learning discriminatory deep clustering models. Presented at International Conference on Computer Analysis of Images and Patterns Springer. https://doi.org/10.1007/978-3-030-29888-3_18
• Recurrent neural networks for financial time-series modelling
  
  Tsang, G., Deng, J., & Xie, X. (2018). Recurrent neural networks for financial time-series modelling. Presented at 2018 24th International Conference on Pattern Recognition (ICPR) IEEE. https://doi.org/10.1109/icpr.2018.8545666
• Local representation learning with a convolutional autoencoder
  
  Kenning, M. P., Xie, X., Edwards, M., & Deng, J. (2018). Local representation learning with a convolutional autoencoder. Presented at 2018 25th IEEE International Conference on Image Processing (ICIP) IEEE. https://doi.org/10.1109/icip.2018.8451233
• A deep convolutional auto-encoder with embedded clustering
  
  Alqahtani, A., Xie, X., Deng, J., & Jones, M. W. (2018). A deep convolutional auto-encoder with embedded clustering. Presented at 2018 25th IEEE international conference on image processing (ICIP) IEEE. https://doi.org/10.1109/icip.2018.8451506
• Labeling subtle conversational interactions within the CONVERSE dataset
  
  Edwards, M., Deng, J., & Xie, X. (2017). Labeling subtle conversational interactions within the CONVERSE dataset. Presented at 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) IEEE. https://doi.org/10.1109/percomw.2017.7917547
• Amd classification in choroidal oct using hierarchical texton mining
  
  Ravenscroft, D., Deng, J., Xie, X., Terry, L., Margrain, T. H., North, R. V., & Wood, A. (2017). Amd classification in choroidal oct using hierarchical texton mining. Presented at International Conference on Advanced Concepts for Intelligent Vision Systems Springer. https://doi.org/10.1007/978-3-319-70353-4_21
• Nested shallow cnn-cascade for face detection in the wild
  
  Deng, J., & Xie, X. (2017). Nested shallow cnn-cascade for face detection in the wild. Presented at 2017 12th IEEE International Conference on Automatic Face \& Gesture Recognition (FG 2017) IEEE. https://doi.org/10.1109/fg.2017.29
• Learning feature extractors for AMD classification in OCT using convolutional neural networks
  
  Ravenscroft, D., Deng, J., Xie, X., Terry, L., Margrain, T. H., North, R. V., & Wood, A. (2017). Learning feature extractors for AMD classification in OCT using convolutional neural networks. Presented at 2017 25th European Signal Processing Conference (EUSIPCO) IEEE. https://doi.org/10.23919/eusipco.2017.8081167
• Detect face in the wild using CNN cascade with feature aggregation at multi-resolution
  
  Deng, J., & Xie, X. (2017). Detect face in the wild using CNN cascade with feature aggregation at multi-resolution. Presented at 2017 IEEE International Conference on Image Processing (ICIP) IEEE. https://doi.org/10.1109/icip.2017.8297067
• Combining stacked denoising autoencoders and random forests for face detection
  
  Deng, J., Xie, X., & Edwards, M. (2016). Combining stacked denoising autoencoders and random forests for face detection. Presented at International Conference on Advanced Concepts for Intelligent Vision Systems Springer. https://doi.org/10.1007/978-3-319-48680-2_31
• Age-related macular degeneration detection and stage classification using choroidal oct images
  
  Deng, J., Xie, X., Terry, L., Wood, A., White, N., Margrain, T. H., & North, R. V. (2016). Age-related macular degeneration detection and stage classification using choroidal oct images. Presented at International Conference on Image Analysis and Recognition Springer. https://doi.org/10.1007/978-3-319-41501-7_79
• 3D interactive coronary artery segmentation using random forests and Markov random field optimization
  
  Deng, J., Xie, X., Alcock, R., & Roobottom, C. (2014). 3D interactive coronary artery segmentation using random forests and Markov random field optimization. Presented at 2014 IEEE International Conference on Image Processing (ICIP) IEEE. https://doi.org/10.1109/icip.2014.7025189
• Protein classification using Hidden Markov models and randomised decision trees
  
  Lacey, A., Deng, J., & Xie, X. (2014). Protein classification using Hidden Markov models and randomised decision trees. Presented at 2014 7th International Conference on Biomedical Engineering and Informatics IEEE. https://doi.org/10.1109/bmei.2014.7002856
• Conversational interaction recognition based on bodily and facial movement
  
  Deng, J., Xie, X., & Zhou, S. (2014). Conversational interaction recognition based on bodily and facial movement. Presented at International Conference Image Analysis and Recognition Springer. https://doi.org/10.1007/978-3-319-11758-4_26
• From clamped local shape models to global shape model
  
  Fang, H., Deng, J., Xie, X., & Grant, P. W. (2013). From clamped local shape models to global shape model. Presented at 2013 IEEE International Conference on Image Processing IEEE. https://doi.org/10.1109/icip.2013.6738725
• Recognizing conversational interaction based on 3D human pose
  
  Deng, J., Xie, X., Daubney, B., Fang, H., & Grant, P. W. (2013). Recognizing conversational interaction based on 3D human pose. Presented at International Conference on Advanced Concepts for Intelligent Vision Systems Springer. https://doi.org/10.1007/978-3-319-02895-8_13

Doctoral Thesis

• Adaptive Learning for Segmentation and Detection
  
  Deng, J. (2017). Adaptive Learning for Segmentation and Detection [Thesis]. Swansea University.

Journal Article

• Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation
  
  Zhang, F. X., Deng, J., Lieck, R., & Shum, H. P. (2025). Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation. IEEE Transactions on Medical Robotics and Bionics, 7(1), 266-280. https://doi.org/10.1109/TMRB.2024.3517137
• Artificial intelligence for geometry-based feature extraction, analysis and synthesis in artistic images: a survey
  
  Vijendran, M., Deng, J., Chen, S., Ho, E. S. L., & Shum, H. P. H. (2025). Artificial intelligence for geometry-based feature extraction, analysis and synthesis in artistic images: a survey. Artificial Intelligence Review, 58(2), Article 64. https://doi.org/10.1007/s10462-024-11051-3
• Sparse representation for restoring images by exploiting topological structure of graph of patches
  
  Gao, Y., Cai, Z., Xie, X., Deng, J., Dou, Z., & Ma, X. (2025). Sparse representation for restoring images by exploiting topological structure of graph of patches. IET Image Processing, 19(1), Article e70004. https://doi.org/10.1049/ipr2.70004
• A survey on vulnerability of federated learning: A learning algorithm perspective
  
  Xie, X., Hu, C., Ren, H., & Deng, J. (2024). A survey on vulnerability of federated learning: A learning algorithm perspective. Neurocomputing, 573, Article 127225. https://doi.org/10.1016/j.neucom.2023.127225
• Image restoration with group sparse representation and low‐rank group residual learning
  
  Cai, Z., Xie, X., Deng, J., Dou, Z., Tong, B., & Ma, X. (2024). Image restoration with group sparse representation and low‐rank group residual learning. IET Image Processing, 18(3), 741-760. https://doi.org/10.1049/ipr2.12982
• FedBoosting: Federated learning with gradient protected boosting for text recognition
  
  Ren, H., Deng, J., Xie, X., Ma, X., & Wang, Y. (2024). FedBoosting: Federated learning with gradient protected boosting for text recognition. Neurocomputing, 569, Article 127126. https://doi.org/10.1016/j.neucom.2023.127126
• Joint multi-label learning and feature extraction for temporal link prediction
  
  Ma, X., Tan, S., Xie, X., Zhong, X., & Deng, J. (2022). Joint multi-label learning and feature extraction for temporal link prediction. Pattern Recognition, 121. https://doi.org/10.1016/j.patcog.2021.108216
• GRNN: generative regression neural network—a data leakage attack for federated learning
  
  Ren, H., Deng, J., & Xie, X. (2022). GRNN: generative regression neural network—a data leakage attack for federated learning. ACM Transactions on Intelligent Systems and Technology (TIST), 13(4), 1-24. https://doi.org/10.1145/3510032
• A directed graph convolutional neural network for edge-structured signals in link-fault detection
  
  Kenning, M., Deng, J., Edwards, M., & Xie, X. (2022). A directed graph convolutional neural network for edge-structured signals in link-fault detection. Pattern Recognition Letters, 153, 100-106. https://doi.org/10.1016/j.patrec.2021.12.003
• Sampling strategies for learning-based 3D medical image compression
  
  Nagoor, O. H., Whittle, J., Deng, J., Mora, B., & Jones, M. W. (2022). Sampling strategies for learning-based 3D medical image compression. Machine Learning With Applications, 8. https://doi.org/10.1016/j.mlwa.2022.100273
• TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain
  
  Wang, Y., Xia, Z., Deng, J., Xie, X., Gong, M., & Ma, X. (2021). TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain. BMC Bioinformatics, 22(9), 1-15. https://doi.org/10.1186/s12859-021-04190-9
• 3D Interactive Segmentation With Semi-Implicit Representation and Active Learning
  
  Deng, J., & Xie, X. (2021). 3D Interactive Segmentation With Semi-Implicit Representation and Active Learning. IEEE Transactions on Image Processing, 30, 9402-9417. https://doi.org/10.1109/tip.2021.3125491
• Estimating the accuracy of a reduced-order model for the calculation of fractional flow reserve (FFR)
  
  Boileau, E., Pant, S., Roobottom, C., Sazonov, I., Deng, J., Xie, X., & Nithiarasu, P. (2018). Estimating the accuracy of a reduced-order model for the calculation of fractional flow reserve (FFR). International Journal for Numerical Methods in Biomedical Engineering, 34(1). https://doi.org/10.1002/cnm.2908
• Fixing the root node: Efficient tracking and detection of 3D human pose through local solutions
  
  Daubney, B., Xie, X., Deng, J., Mac Parthaláin, N., & Zwiggelaar, R. (2016). Fixing the root node: Efficient tracking and detection of 3D human pose through local solutions. Image and Vision Computing, 52, 73-87. https://doi.org/10.1016/j.imavis.2016.05.010
• From pose to activity: Surveying datasets and introducing CONVERSE
  
  Edwards, M., Deng, J., & Xie, X. (2016). From pose to activity: Surveying datasets and introducing CONVERSE. Computer Vision and Image Understanding, 144, 73-105. https://doi.org/10.1016/j.cviu.2015.10.010
• A bag of words approach to subject specific 3D human pose interaction classification with random decision forests
  
  Deng, J., Xie, X., & Daubney, B. (2014). A bag of words approach to subject specific 3D human pose interaction classification with random decision forests. Graphical Models, 76(3), 162-171. https://doi.org/10.1016/j.gmod.2013.10.006