Outputs (5)

Artificial intelligence for geometry-based feature extraction, analysis and synthesis in artistic images: a survey (2024)
Journal Article
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

Artificial Intelligence significantly enhances the visual art industry by analyzing, identifying and generating digitized artistic images. This review highlights the substantial benefits of integrating geometric data into AI models, addressing challe... Read More about Artificial intelligence for geometry-based feature extraction, analysis and synthesis in artistic images: a survey.

Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation (2024)
Journal Article
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

Surgical workflow anticipation is the task of predicting the timing of relevant surgical events from live video data, which is critical in Robotic-Assisted Surgery (RAS). Accurate predictions require the use of spatial information to model surgical i... Read More about Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation.

Centersam: Fully Automatic Prompt for Dense Nucleus Segmentation (2024)
Presentation / Conference Contribution
Li, Y., Ren, H., Deng, J., Ma, X., & Xie, X. (2024, May). Centersam: Fully Automatic Prompt for Dense Nucleus Segmentation. Presented at 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece

Nucleus segmentation is a vitally important task in biomedical image analysis which leads to multiple applications such as cellular behavior study, tumor detection and cancer diagnosis. However, challenges, such as ambiguous boundary for touching or... Read More about Centersam: Fully Automatic Prompt for Dense Nucleus Segmentation.

ST-SACLF: Style Transfer Informed Self-Attention Classifier for Bias-Aware Painting Classification (2024)
Book Chapter
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

A survey on vulnerability of federated learning: A learning algorithm perspective (2024)
Journal Article
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

Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centrali... Read More about A survey on vulnerability of federated learning: A learning algorithm perspective.