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  ## Mirror
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  <https://www.modelscope.cn/datasets/Genius-Society/aal_stats_vol>
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  ## Reference
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  [1] [Chapter II ‐ Classifying AD patients and normal controls from brain images](https://github.com/Genius-Society/medical_image_computing/blob/ad/README.md)
 
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  ## Mirror
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  <https://www.modelscope.cn/datasets/Genius-Society/aal_stats_vol>
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+ The raw data of this dataset locates at `./data` in the above mirror repo, whose details are as follows:
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+ ### Attachments
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+ 1. Original data;
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+ 2. Packages;
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+ 3. Intermediate products;
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+ 4. Source code with the output csv data.
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+
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+ ### Steps to Achieve the Goal
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+ 1. Skull Stripping: To extract brain from raw MRI images;
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+ 2. Tissue Segmentation: To segment brain into white matters, grey matters and cerebrospinal fluid (CSF);
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+ 3. Registration: To register the standard space to native space;
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+ 4. Measurement(With mask): With the generated masks, to measure the volumes of 90 ROIs;
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+ 5. Classification: To fill out the shell scripts and run them, to write a Python script to train the model and test with the provided testing dataset.
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+
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+ ### Classification details
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+ 0. If everything went well before this, a matrix with a size of 50x90 would be obtained: It means there are 50 samples of which each has 90 features(volumes);
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+ 1. Dataset: 40 Training and 10 Testing;
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+ 2. Training: To feed into the classifier to train a model;
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+ 3. Testing: To test the model and provide the result.
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+
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  ## Reference
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  [1] [Chapter II ‐ Classifying AD patients and normal controls from brain images](https://github.com/Genius-Society/medical_image_computing/blob/ad/README.md)