Fusing Multi-Modal Medical Data with Reliable AI
Postdoctoral Research Associate at Imperial College London. I develop trustworthy AI frameworks - focusing on uncertainty quantification and multi-modal fusion - to ensure state-of-the-art deep learning models are reliable, explainable, and safe.
Multi-Modal Fusion
Synthesising complementary information from medical imaging (MICCAI/ISBI) and clinical records. I develop fusion architectures that robustly integrate diverse data modalities.
Uncertainty Quantification
Designing deep learning frameworks that rigorously quantify uncertainty. My work ensures AI systems are aware of their own limitations, crucial for safe clinical deployment.
Medical Foundation Models
Adapting state-of-the-art Large Language Models (LLMs) for healthcare. I focus on fine-tuning foundation models to be both explainable and clinically accurate.
Recent News
Featured Research
Deep Evidential Fusion for Multimodal Medical Image Segmentation
Explore Dr. Ling Huang's featured 2024 research published in the top-tier journal Information Fusion. This pivotal paper addresses the critical "black box" problem in modern healthcare by introducing a novel Deep Evidential Fusion framework specifically designed for multimodal medical image segmentation.
Discover how this advanced approach goes beyond standard predictions by explicitly quantifying uncertainty and automatically learning the reliability of different imaging sources. By shifting the focus from pure accuracy to clinical trustworthiness, Dr. Huang's work paves the way for safer, more transparent AI-driven decision support systems in complex medical environments.
Dive DeeperSelected Publications
Ling Huang, Yucheng Xing, Qika Lin, Su Ruan, Mengling Feng
Yucheng Xing, Ling Huang, Jingying Ma, Ruping Hong, Jiangdong Qiu, Pei Liu, Kai He, Huazhu Fu, Mengling Feng
