Difference between revisions of "2017 Winter Project Week/DWI Similarity Metrics"

Latest revision as of 04:36, 13 January 2017

Home < 2017 Winter Project Week < DWI Similarity Metrics

Objective	Approach and Plan	Progress and Next Steps
Investigate possible metrics for identification of information-rich patches in DWI data	Discuss about DWI data structure and acquisition protocol to have a better understanding of the information available in DWI datasets (e.g. single shell / multi shell, gradient orientations, b-values) Identify possible metrics available to measure similarity (e.g. Mutual Information, Correlation) between datasets Investigate the potential of CNN for this type of application	Discussed with Lauren and clarified some points on Diffusion Imaging. It also might help me to optimize some of my code. Discussed with Mohsen about machine learning and how I might use it for data classification in my project. Discussed with Henrik about information-rich patches of information in DWI data, and I learned about his LORDWI approach that could be useful to help me identify relevant information in DWI datasets. Attended almost all machine learning events, and got a better understanding of their utility and when it might be useful to use them and when it is not necessary. Participated in the tutorial contest.

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 * Discussed with Henrik about information-rich patches of information in DWI data, and I learned about his LORDWI approach that could be useful to help me identify relevant information in DWI datasets.
 * Attended almost all machine learning events, and got a better understanding of their utility and when it might be useful to use them and when it is not necessary.
-* Participated at the tutorial contest.
+* Participated in the tutorial contest.
 |}
 ==Background and References==
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