Dr Qingjie Meng

Supervisor Details

Contact Details

School of Computer Science, University of Birmingham

Scientific Background

Research Interests

Qingjie Meng’s research focuses on Medical AI, combining deep learning, artificial intelligence, computer vision, medical imaging and healthcare.

Key research themes include:

Digital twin
Generative AI models
Representation learning
Transfer learning
Multimodal learning

For more about Dr Meng, please follow visit Qingjie Meng’s webpage.

Research Groups

Qingjie Meng

Supervision Style

In three words or phrases: supportive, student-centred, collaborative.

Provision of Training

Early in the PhD, I provide structured, hands-on guidance to build a strong technical foundation. As your confidence grows, you’ll be encouraged to take the lead on your research, supported by a collaborative lab environment where peer learning is actively fostered.

Progression Monitoring and Management

I will set out my expectations of your progress on a weekly basis. If these exceptions are not met, we’ll work jointly to adjust the plan and find a constructive way forward.

Communication

We use a group chat to stay connected and share updates throughout the week, including weekends when needed. While I encourage you to stay engaged with team communications, maintaining a healthy work/life balance is equally important.

Meetings

PhD Students can expect scheduled meetings with me at least once per week. These meetings will be a mixture of face-to-face and via video chat or telephone. I am usually contactable for an instant response on every working day.

Work Patterns

The timing of work in my lab is completely flexible, and (other than attending pre-arranged meetings), I expect students to manage their own time.

Notice Period for Feedback

I need at least 2 weeks' notice to provide feedback on written work of up to 5,000 words.

MIBTP Project Details

Primary supervisor for:

Generative AI for Synthetic Microscopy image of Healthy Oral Mucosa

See the PhD Opportunities section to see if this project is currently open for applications via MIBTP.

Please Note: The main page lists projects via BBSRC Research Theme(s) quoted and then relevant Topic(s).

Generative AI for Synthetic Microscopy image of Healthy Oral Mucosa

Secondary Supervisor(s): Dr Rasha Abu-Eid

University of Registration: University of Birmingham

BBSRC Research Themes:

Understanding the Rules of Life (Systems Biology)

Project Outline

Understanding the structure of healthy oral mucosa is essential for studying function and physiological changes over time. It also provides a foundation for identifying pathological changes, supporting the discovery of disease markers and therapeutic targets. However, obtaining healthy oral mucosa samples is challenging, as biopsies from healthy tissues are rarely taken. Most studies rely on surrogate tissues from lesion margins or benign conditions, which may not fully reflect healthy tissue. This limits our ability to build high-quality reference datasets. With advances in artificial intelligence (AI), it is now possible to generate realistic synthetic images, offering an ethical and scalable way to expand healthy tissue collections and support future research. This project will use generative AI to establish a large reference microscopy images dataset of healthy oral mucosa.

Aims and Objectives: The project aims to develop AI models that replicate the appearance and structure of healthy oral mucosa. Objectives include:

1. Training generative models to produce realistic, high-resolution microscopy images.

2. Ensuring no synthetic image is identical to real patient data.

3. Evaluating image quality, diversity, and biological relevance using standard metrics and expert review.

Methods: Anonymised digital images from tissues in biobanks will be used to train generative models on university computing resources, including 200+ NVIDIA A100 GPUs and group workstations. Image quality will be assessed using quantitative metrics and clinical expert qualitative review. Privacy safeguards will be built into the generation process.

Outcomes and Impact: This multidisciplinary project will deliver deployable models, reproducible methods, and, where allowed, shareable datasets. The student will gain training in deep learning, AI, image analysis, microscopy, digital pathology, and oral mucosa biology. Generic skills training is provided through the Postgraduate School.

APPLY HERE