Welcome to BIPL at UoL
BIPL(Biomedical Image Processing Lab) mainly works on the development of novel algorithms and application tools for automated processing of medical and biological signals/images. Major research contribution is made towards two complementary aspects: Fundamental understanding and modelling of images and signals, and Application-driven development in collaboration with professionals in medicine, biology, psychology, engineering, physics, earth observation and sociology. With digital biomedical signals/images being widely stored and shared in networks, the research group also looks to bolster the analysis of biomedical data and cyber-security of signal processing systems.
There are four main areas of research:
- Medical signal analysis
- Animal modelling
- Remote sensing
- Cyber-security
Joining BIPL
If you are interested in joining please go to the recruitment page.
Funding
We are grateful for funding from UK EPSRC, ESRC, AHRC, MRC, EU ICT, Royal Society, Innovate UK, Leverhulme Trust, Invest NI, Puffin Trust, Alzheimer Research (UK) and industry.
News
Feb 4, 2025Zhihua Liu has passed his viva today and will be awarded the PhD degree subject to minor amendments, congratulations!
Feb 3, 2025One paper is published: Zhang, M., Bzura, A., Baitei, E.Y. et al. A gut microbiota rheostat forecasts responsiveness to PD-L1 and VEGF blockade in mesothelioma. Nat Commun 15, 7187 (2024).Accepted Version
Feb 3, 2025One paper is published: Y. Ju, K. -M. Lam, W. Xie, H. Zhou, J. Dong and B. Shi, Deep Learning Methods for Calibrated Photometric Stereo and Beyond, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 11, pp. 7154-7172, Nov. 2024.Accepted Version
Feb 3, 2025One paper is published: F. Zhou, Z. Jiang, H. Zhou and X. Li, SMC-NCA: Semantic-Guided Multi-Level Contrast for Semi-Supervised Temporal Action Segmentation, in IEEE Transactions on Multimedia, vol. 26, pp. 11386-11401, 2024.Accepted Version
Feb 3, 2025One paper is published: F. Chen, H. Balzter, P. Ren and H. Zhou, SRCNet: Seminal Image Representation Collaborative Network for Oil Spill Segmentation in SAR Imagery, in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-18, 2024.Accepted Version