Summary
Dr Qingde Li has been a lecturer in computer science at the University of Hull since 2001. Previously he was Professor and Deputy Head of Mathematics and Computer Science at Anhui Normal University, China.
Dr Li's research findings have been published in the world's most prestigious graphics journals including ACM Transactions on Graphics, IEEE Transactions on Visualization and Computer Graphics, and IEEE Transactions on Medical Imaging.
Dr Li@Google scholar: https://scholar.google.co.uk/citations?user=abSAMUkAAAAJ&hl=en
Journal Article
ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation
Li, Z., Zheng, Y., Shan, D., Yang, S., Li, Q., Wang, B., …Shen, D. (in press). ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation. IEEE Transactions on Medical Imaging, https://doi.org/10.1109/tmi.2024.3363190
Using outlier elimination to assess learning-based correspondence matching methods
Ding, X., Luo, Y., Jie, B., Li, Q., & Cheng, Y. (2024). Using outlier elimination to assess learning-based correspondence matching methods. Information Sciences, 659, Article 120056. https://doi.org/10.1016/j.ins.2023.120056
LViT: Language meets Vision Transformer in Medical Image Segmentation
Li, Z., Li, Y., Li, Q., Wang, P., Guo, D., Lu, L., …Hong, Q. (2024). LViT: Language meets Vision Transformer in Medical Image Segmentation. IEEE Transactions on Medical Imaging, 43(1), 96-107. https://doi.org/10.1109/TMI.2023.3291719
A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification
Hong, Q., Lin, L., Li, Z., Li, Q., Yao, J., Wu, Q., …Tian, J. (in press). A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification. IEEE Transactions on Neural Networks and Learning Systems, https://doi.org/10.1109/TNNLS.2023.3280646
Consensus Adversarial Defense Method Based on Augmented Examples
Ding, X., Cheng, Y., Luo, Y., Li, Q., & Gope, P. (2022). Consensus Adversarial Defense Method Based on Augmented Examples. IEEE Transactions on Industrial Informatics, https://doi.org/10.1109/TII.2022.3169973
Research interests
Dr Li's current research interests are in the areas of computer graphics, visual and tangible computing, as well as their applications in mixed reality environment , including
(1). High precision geometric reconstruction of vascular and neural structures.
(2). Deep learning on shape understanding and geometric information processing.
(3). Tactile modelling and haptic effects programming.
(4). Environmental geometry-aware wearable devices.
Postgraduate supervision
Dr Li welcomes applications in the areas of visual and tangible computing, deep learning in geometric information processing and scientific simulation, cloud-based collaborative geometric design in mixed reality environment, as well as developing environmental geometry-aware wearable devices.