معرفی
Qianni Zhang is a Senior Lecturer and PhD/PDRA Engagement Lead at the Centre for Multimodal AI within the School of Electronic Engineering and Computer Science at Queen Mary University of London. She leads research initiatives at the intersection of artificial intelligence and healthcare applications, with a particular focus on medical image analysis.
Dr. Zhang's research interests center on machine learning and computer vision with specific applications in medical image computing. Her work spans multiple domains including medical imaging analysis for cardiology, ophthalmology, and oncology. She has developed numerous deep learning approaches for image segmentation, registration, and analysis, particularly focused on intravascular ultrasound, optical coherence tomography, and MRI applications. Her research bridges theoretical computer vision with practical clinical needs, creating tools that have potential to improve diagnostic accuracy and treatment planning in healthcare settings.
Her recent publications demonstrate a clear trajectory toward increasingly sophisticated multimodal AI approaches for medical imaging. The trend shows progression from basic image segmentation techniques to complex fusion networks that combine multiple imaging modalities and incorporate anatomical knowledge. Many of her papers focus on cardiovascular applications, particularly intravascular ultrasound analysis, while others address ophthalmological and general medical imaging challenges. The consistent theme across her work is the application of cutting-edge deep learning techniques to solve real-world medical imaging problems with clinical relevance.
- ARCNET (Barts and the London Charity, £15,496, 2025-2026)
- Deep Learning based Multispectral Image Analysis for Retinal Lesion Detection (Royal Society, £12,000, 2022-2025)
- Anatomical And Functional Analysis Of The Soft Palate (Barts Health NHS, £132,620, 2022-2025)
- Machine learning for improved graphics in sport broadcast (Innovate UK, £214,061, 2022-2024)
Dr. Zhang supervises a diverse group of PhD students working on various aspects of medical image computing and multimodal AI. Her students' projects span multiple healthcare domains including cardiovascular imaging, ophthalmology, dentistry, and general medical diagnostics. She has established herself as a leading researcher in applying deep learning to medical imaging challenges, with particular expertise in segmentation, registration, and multimodal fusion techniques for clinical applications.
