Official PyTorch implementation of "Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging". LDAE is a novel unsupervised framework for 3D medical imaging that combines a latent diffusion model with semantic controls.

Deep learning-based tool for automated segmentation of sclerosis lesions in 3D MRI brain scans

Knee bone and cartilage segmentation in 3D MRI

[Graduation Thesis @ VLUTE] A full-stack Next.js & FastAPI GUI for Alzheimer's disease classification using 3D MRI & clinical data. Integrated with Quantum Machine Learning and Explainable AI (Grad-CAM, Grad-CAM++, Ablation-CAM). Student: [Nhan Quoc Viet] | Advisor: M.Sc. Nguyen Khac Tuong

Quantitative analysis of 3D vascular tree images using texture features. Implementation of methods for classifying blood vessel structures and monitoring physical parameters using 3D co-occurrence matrices, run-length matrices, and gradient-based features. Includes simulated vessel tree models and analysis of real confocal microscopy data.