Herbert Wertheim College of Engineering
Gong Lab
Led by Kuang Gong · Biomedical Engineering
What we study
The Gong Lab uses deep learning and medical imaging to improve disease diagnosis and treatment, focusing on cancer and Alzheimer’s. They develop methods like physics-informed deep learning and multi-modality data integration.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Project
- 1
- Papers
- 5
Current projects
Diffusion models-based image reconstruction
Improving Gallium-68 PET imaging for cancer diagnosis with diffusion models.
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- TauGenNet: Plasma-Driven Tau PET Image Synthesis via Text-Guided 3D Diffusion Models
2026 · IEEE Transactions on Radiation and Plasma Medical Sciences
Creates synthetic tau PET images from MRI and plasma data using a text-guided 3D model.
- PET Image Reconstruction Using Deep Diffusion Image Prior
2026 · IEEE Transactions on Medical Imaging
Uses a diffusion model to reconstruct PET images from low-quality data with less tracer variability.
Show 3 more papersShow fewer
- Cross-Modality Deep Learning Denoising for Low-Dose μSPECT: Transfer of PET-Trained U-Net and Diffusion Models
2026 · Chemical & Biomedical Imaging
Tests whether a U-Net and diffusion model can improve low-dose μSPECT images by training on PET data.
- CDPDNet: Integrating text guidance with hybrid vision encoders for medical image segmentation
2026 · Computerized Medical Imaging and Graphics
Develops a model that uses text guidance to improve medical image segmentation.
- LDM-Morph: Latent diffusion model guided deformable image registration
2025 · Pattern Recognition
Improves medical image registration by using a latent diffusion model to align features.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Kuang Gong hasn't set up this page yet. Their UF Scholars profile lists their publications.