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Pattern recognition for clinical neuroimaging

A major application of pattern recognition in neuroimaging is the design of decision systems to support clinical decisions. Such approaches aim to improve diagnosis and prognosis of brain diseases such as Alzheimer’s disease, schizophrenia or autism for instance. The aim of this course is to teach how to design and validate pattern recognition for clinical applications in brain diseases.

The slides, Jupyter notebook and example dataset can be found in this Google drive folder.

To go further, check this repository, which contains a software framework for reproducible machine learning experiments on automatic classification of Alzheimer's disease using multimodal MRI and PET data from three publicly available datasets (ADNI, AIBL and OASIS).