Hello Folks !
I have two different brain MR image databases acquired
across two different countries. I need to perform patch based supervised
binary classification task (+ pathology and - Normal). The 1st database
contains both +pathology patients and -normal subjects whereas second
database contains only +pathologysubjects. I have completed the
classification task on 1st database. Now I want to analyze the second
database. Since there is no normal/healthy data in later case, I need to
use that data from 1st database. There exists a dataset bias. Well,
following are the questions
1) Is it the classic case of "co-variate shift adaptation"?
2) I went through some papers by Sugiyama and Yamada "No Bias Left Behind:
Co-variate Shift Adaptation". But I am not sure about the scalability of
those algos to my very high dimensional MRI data. Some proposed the "Zero
Shot Learning".
3) How to go about this problem? How do you people tackle data-set bias?
All thoughts are welcome!
--
Warm Regards
Yogesh Karpate
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