Yinghua Fu

Yinghua Fu, PhD, serves as a Research Fellow in the Department of Diagnostic Radiology and Nuclear Medicine, focusing on advanced medical image analysis through computational methods. Her work bridges clinical medicine and artificial intelligence to develop practical diagnostic tools.

Her research centers on deep learning applications for medical imaging, particularly in diabetic retinopathy analysis, Alzheimer's disease classification, and retinal image change detection. Key innovations include DAGU-Net for segmentation efficiency, TSCA-Net for attention-based spatial-channel processing, and lightweight architectures like MirageNet that minimize computational demands while maintaining accuracy. She specializes in adapting computer vision techniques to clinical constraints, emphasizing model interpretability and real-world deployability.

Analysis of her 15 most recent publications (2023-2026) reveals three dominant trends: (1) Multi-scale feature extraction for medical image segmentation, (2) Lightweight network design targeting edge deployment in clinical settings, and (3) Cross-modal fusion techniques integrating structural and functional imaging data. Her work consistently addresses diabetic retinopathy and neurodegenerative disorders through retinal and brain imaging biomarkers.

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