About team MIA
● Mission:
To develop advanced deep learning methods for medical image analysis, with a focus on MRI and related modalities
● Scope:
The entire medical imaging pipeline including image generation, segmentation, diagnosis, and survival prediction
● Goal:
To support more accurate and personalized healthcare through flexible and robust medical imaging models
Available internship topics
● MRI image generation using deep learning techniques
● MRI image registration for spatial alignment across longitudinal scans
Our research topics
Cross-modality Image Translation
● We address the challenge of limited annotated data in medical image segmentation by applying unsupervised domain adaptation through cross-modality image translation.
Related publications
[Scientific Reports'24] Target-Aware Cross-Modality Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation
Post-operative MRI Generation
● We generate post-operative MRI from pre-operative scans to help neurosurgeons estimate potential outcomes before surgery, enabling more effective surgical and treatment planning.
Related publications
[MICCAI'25] Pre-to-Post Operative MRI Generation with Retrieval-based Visual In-Context Learning
Multi-modality fusion
● Our research addresses the challenges of noise and artifacts in rs-fMRI data by proposing a comprehensive deep learning framework that integrates spatial, spectral, and temporal features to enhance the detection of noise-related components.
Related publications
[JBHI'24] A Unified Multi-Modality Fusion Framework for Deep Spatio-Temporal-Spectral Feature Learning in Resting-State fMRI Denoising
Survival prediction
● We focus on survival prediction in medical imaging by combining multimodal MRI with clinical or genetic data. This allows us to capture both spatial tumor characteristics and biological context, leading to more accurate and personalized predictions.
Related publications
[SMC'25] Multimodal Integration of MRI and Genetic Information for Glioblastoma Survival Prediction