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Lei Zhang

What I cannot create，I do not understand.--Richard Feynman
alt text 

Senior Lecturer
School of Computer Science, University of Lincoln,
Lincoln, UK
Principal AI Research Scientist
MSK Doctors, The Keep Clinic, UK
E-mail: leizhangtech@gmail.com

About me

I am a Senior Lecturer at the Department of Computer Science, University of Exeter. Previously, I was a Senior Lecturer at the University of Lincoln and a member of the Laboratory of Vision Engineering (LoVE) research group. My work focuses on developing scalable and interpretable AI solutions that are theoretically robust, practically applicable, and beneficial to healthcare professionals and patients. My dedication lies in applying AI techniques, particularly in Multimodal, Interpretable, and Causal AI, to address complex healthcare challenges. I have been pivotal in securing grants and driving development in numerous multidisciplinary projects, including those funded by EPSRC, Innovate UK, BBSRC, and CRUK, etc.

Currently, I also work as an AI research scientist in hospitals and clinics, where I provide consultancy on technical AI solutions, lead the development of scientific algorithms, and create commercial software tools to address challenging healthcare problems.

My leadership in interdisciplinary teams fosters the exchange of knowledge and skills across different domains.

Research

Research interests

  • Artificial Intelligence (AI) in Heathcare

  • Machine Learning

  • Multimodal learning

  • Causal inference

Working papers

  • Multimodal Autoregressive Pretraining for Large-Scale Vision Encoders in medical images.

  • Efficient VLM with Adaptive Structured Pruning and Dynamic Compute Allocation

  • Constraint Synthetic data using Probabilistic Causal Models mitigates the models collapse

  • Better alignment in Vision-Language Models boosts reasoning Medical Image Segmentation

Under review

  1. Q.A. Tang, L. Zhang, et.al. "Learning and Inferring Counterfactuals using causal structure model from Multimodal Data: Enhancing Treatment Planning for Lung Disease

  2. Y.F Zhu, L. Zhang et.al."Counterfactual Medical Images Generation for Lung Disease Diagnosis Using Probabilistic Causal Models and Active Learning".

  3. Dw Lv,..., L. Zhang, J. Yang et.al. "MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images, CVPR 2025

Selected publications

  1. Q. Hao, L. Yua, S. Tian, and L. Zhang, "SEDyConv: Spatially Enhanced Dynamic Convolution for Medical Multi-Organ Segmentation in CTs," Knowledge-Based Systems, 2025, (Accepted).

  2. G. Liu, W. Huang, Y. Li, L. Zhang,Y. Wang and J.Z. Hu "A weakly-supervised follicle segmentation method in ultrasound images," Scientific Reports, vol. 15, p. 13771, 2025. https://doi.org/10.1038/s41598-025-95957-0

  3. J. Zhong, W. Tian, Y. Xie, Z. Liu, J. Ou, T. Tian, and L. Zhang, "PMFSNet: Polarized Multi-scale Feature Self-attention Network For Lightweight Medical Image Segmentation," Computer Methods and Programs in Biomedicine, 261, 108611, 2025.

  4. S. Li, L. Wang, JY Wang, ZH.Zhang, J. Zhang, and L. Zhang, "Enhanced Anomaly Detection in 3D Motion through Language-Inspired Occlusion-Aware Modeling" MLLMA: Special Session on Multimodal Large Language Models and Applications, MMM, 2024.

  5. P.J LV and L. Zhang, "MetaUNETR: Rethinking Token Mixer Encoding for Efficient Multi-organ Segmentation," Proc. MICCAI 2024,pp 446–455, 2024.

  6. K. Armstrong, L. Zhang, Y. Wen, A. P. Willmott, P. Lee, and X. Ye, "A Marker-less Human Motion Analysis System for Motion-based Biomarker Identification and Quantification in Knee Disorders," Frontiers in Digital Health, vol. 6, p. 1324511, 2024.

  7. K. Armstrong, L. Zhang, P. Lee, and X. Ye, "Zero-dimensional Biomarker-based Medical Action Recognition: Towards More Explainable AI in Healthcare," Proc. 10th Int. Conf. Bioinformatics, 2023.

  8. Y. Wen, L. Zhang, X. Meng, and X. Ye, "Rethinking the Transfer Learning for FCN Based Polyp Segmentation in Colonoscopy," IEEE Access, vol. 11, pp. 16183-16193, 2023.

  9. W. Duan, L. Zhang, J. Colman, G. Gulli, and X. Ye, "MidFusNet: Mid-dense Fusion Network for Multi-modal Brain MRI Segmentation," Proc. Int. MICCAI Brainlesion Workshop, pp. 102-114, 2022.

  10. L. Zhang and Y. Wen, "A Transformer-based Framework for Automatic COVID-19 Diagnosis in Chest CTs," Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), pp. 513-518, 2021.

  11. W. Duan, L. Zhang, J. Colman, G. Gulli, and X. Ye, "Multi-Modal Brain Segmentation Using Hyper-Fused Convolutional Neural Network," Proc. 4th Int. Workshop Mach. Learn. Clin. Neuroimaging, 2021 MICCAI Workshop, Strasbourg, France, Sep. 2021.

  12. J. Colman, L. Zhang, W. Duan, and X. Ye, "DR-Unet104 for Multimodal MRI Brain Tumor Segmentation," Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, Cham, pp. 410-419, 2021.

  13. L. Zhang, G. Yang, and X. Ye, "Automatic Skin Lesion Segmentation by Coupling Deep Fully Convolutional Networks and Shallow Network with Textons," J. Med. Imaging, vol. 6, no. 2, p. 1, 2019.

  14. L. Zhang, H. Gray, X. Ye, L. Collins, and N. Allinson, "Automatic Individual Pig Detection and Tracking in Pig Farms," Sensors, vol. 19, no. 5, p. 1188, 2019.

  15. M. Soltaninejad, L. Zhang, T. Lambrou, G. Yang, N. Allinson, and X. Ye, "MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks," in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Crimi A., Bakas S., Kuijf H., Menze B., Reyes M. (eds), BrainLes 2017. Lecture Notes in Computer Science, Springer, Cham, 2018.

  16. L. Zhang, N. Dudley, T. Lambrou, N. Allinson, and X. Ye, "Automatic Image Quality Assessment and Measurement of Fetal Head in Two-dimensional Ultrasound Image," J. Med. Imaging, vol. 4, no. 2, p. 02401, 2017.

  17. L. Zhang, S. Dolwani, and X. Ye, "Automated Polyp Segmentation in Colonoscopy Frames Using Fully Convolutional Neural Network and Textons," Proc. Medical Image Understanding and Analysis (MIUA), Springer, Cham, pp. 707-717, 2017.

  18. L. Zhang, X. Ye, T. Lambrou, W. Duan, N. Allinson, and N. Dudley, "A Supervised Texton Based Approach for Automatic Segmentation and Measurement of the Fetal Head and Femur in 2D Ultrasound Images," Phys. Med. Biol., vol. 61, no. 3, p. 1095, 2016.

  19. L. Zhang, M. Fisher, and W. Wang, "Retinal Vessel Segmentation Using Multi-scale Textons Derived from Keypoints," Comput. Med. Imaging Graph., vol. 45, 2015.

Full list of publications in Google Scholar.

Projects

    • Causal Counterfactual visualisation for human causal decision making: A case study in healthcare (EPSRC, 2023-2025)

      • Led the development of an AI framework for enhancing human decision-making in disease diagnosis through causal counterfactual reasoning.

    • Biomarker Discovery for Musculoskeletal (MSK) Disorders (Innovate UK, 2023-2025)

      • Led a collaborative effort to identify and validate biomarkers based on multimodal data (Text, MRIs and Motion) for the diagnosis and severity grading of MSK disorders.

    • AI-based Diagnosis for Cartilage Lesion Detection. (EPSRC DTP, 2020-2024)

      • Led a team to develop an AI-based support system for detecting knee MRI cartilage lesions and automated rehabilitation assessment with Biomarkers.

    • Predicting the location of lung nodule occurrence from low-dose CT using Deep learning (CRUK, 2019-2021)

      • Led the development of a deep learning model to predict the location and likelihood of lung nodule formation based on multi-modal data low-dose CT scans and demographic data.

    • PRaVDA: AI-based Proton Radiotherapy Verification and Dosimetry Applications. (Wellcome Trust, 2018-2019)

      • Collaborated with a research team in developing deep learning-based methodologies to optimize proton imaging and reconstruction.

    • PIGSustain: Predicting the Impacts of Intensification and Future Changes on UK Pig Industry Resilience. (ESRC and BBSRC, 2017-2020)

      • Managed a multidisciplinary project, focusing on discovering cues to predict disease outbreaks in pig farms. Our AI-driven tools are aiding in the disease understanding and development of sustainable practices in animal wellbeing.

    • Bowel cancer: Automatic polyp detection and analysis in colonoscopy (CRUK, 2016)

      • This work has not only improved the efficiency and accuracy of polyp detection but also contributed to the early diagnosis and treatment of colorectal cancer.

    • Trainable Vision-based Anomaly Detection and Diagnosis (TADD) project. (Innovate UK, 2015-2018)

      • Led the development of a trainable embedded system for real-time automatic anomaly detection on labels through imaging.

Teaching experience：

• Fellow of the Higher Education Academy (FHEA): Recognized for excellence in teaching and supporting learning in higher education.

• Module Coordinator: Led core modules including Advanced Machine Learning and Algorithm and Complexity. Improved student engagement and learning outcomes by integrating real-world AI applications.

• SSM AI Module Leader: Directed a team to deliver AI modules to medical students from Nottingham Medical School, enhancing interdisciplinary learning and application of AI in healthcare.

Work experience

  1. Senior Lecturer, Department of Computer Science, University of Exeter, UK, 2025-present

  2. Senior Lecturer, School of Computer Science, University of Lincoln, UK, 2020-2025

    • Research and teaching role: Conduct research, teach courses, supervise students, secure funding, engage in administration, and collaborate with industry and academic partners.

  3. Principal AI Research Scientist, MSK Doctors, Keep Clinc, 06.2021-Present

    • Development and Deployment of Multimodal AI Solutions in Clinical Settings

  4. Research Fellow, School of Computer Science, University of Lincoln, UK, 2014-2020

    • Algorithm and System Development in Various Multidisciplinary Projects, Including Those Funded by EPSRC, BBSRC, Innovate UK

  5. Teaching Assistant, School of Computer Science, University of East Anglia, UK, 2011-2014

    • Prepared teaching materials and supported the delivery of lectures and workshops for the modules Database Systems, Fundamental Programming, and Operating Systems.

  6. Associate Manager, QMAP information technologies, Shanghai, China, 2009-2010

    • Managed a team to develop a GIS system API for China Telecom and was responsible for developing the core search engine for data retrieval and analysis.

Degrees and Qualifications:

• PhD – Biomedical engineering and informatics, UEA,UK, 2014

• MSc – Medical image processing and analysis, UEA, UK, 2008

• BSc – Data analysis and information system, NAU, China, 2007


A brief cv.