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Research
Interdisciplinarity is what drives modern research...
Research at DeepMI Lab
Research Domains
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AI in Medical Imaging, Deep-Learning, Convolutional Neural Networks,
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Medical Image Computing, Computational NeuroImaging, Big Data Analysis,
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Machine Learning, Computer Vision and Graphics, Computational Statistics,
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Differential and Computational Geometry, Computational Topology,
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Human Computer Interaction, Geometric Modeling, Computer-aided Design
Research Directions
In close collaboration with clinical and industrial partners, we develop the next-generation computational techniques for the analysis of large biomedical image datasets, including:
- Development of innovative methods for reliable image acquisition, processing, analysis and interpretation – in particular sensitive longitudinal analysis, registration, reconstruction, segmentation and predictive modeling.
- Deep- and machine-learning, computer vision and statistical modeling for the extraction of biomarkers and other clinically relevant information from large data sets (big data) – in particular for computer-aided diagnosis (at presymptomatic stages) and prognosis, personalized medicine, treatment planning, patient stratification, and identification of risk- or preserving factors of neurodegenerative disease.
- Our research focuses on improving our understanding of brain development, neurodegeneration (aging, dementia, Alzheimer's disease, Huntington's disease), the sensitive quantification of subtle drug effects, and improving tumor treatment assessment, via multi-modal imaging.
Methodology
Applications
Neurodegeneration and Disease
Tumor Treatment Assessment
Computer-aided Diagnosis / Prognosis
Drug Research and Personalized Medicine
Optical Coherence Tomography
Neuroanatomical Biometrics
Genetics and Heritability
Research Funding
- National Institutes of Health (NCI, NIBIB, ...)
- Harvard NeuroDiscovery Center
- Massachusetts Alzheimer's Disease Research Program
- Genentech Foundation
- NVIDIA
- Alexander von Humboldt Foundation
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