Week 3: Datasets and Directives

Hi all! This week, I compiled various datasets of tau-PET scans across different platforms/websites, and began requesting them with the help of my advisors. Specifically, the biggest find had to be a website that detailed close to 50 datasets and where to find them. This was a HUGE help, and I am extremely excited about this find. In the next couple weeks, I will begin requesting from all of them in order to use their data for my project. As for my Machine Learning course, I have completed the third and final week, and now am beginning to start setup of the ML model through applications like Google Colab, VSCode, and Jupyter Notebooks! 

Now, where do these datasets fit in the grand scheme of things? With these datasets, I plan to use a 80/20 training-testing split, after generalization of course. Generalization here is probably the most important part of making sure the model works, as if it isn’t generalized, the model might interpret the data completely wrong(it might differentiate between different platforms used to make the brain scan rather than structures solely). In order to accomplish this, we must use tools like dcm2niix and NiBabel. They sound fancy, but in reality are actually simple, but powerful processes! Dcm2niix is a conversion tool, NiBabel is a python library for neuroimaging that has a few key formats(hence the reason for the Dcm2niix converter). Once these scans have been standardized, I will compute SUVR(Standardized Value Uptake Ratio) using the cerebellar reference region as well as the z score for the brain mask. ComBat Harmonization will also probably be used to remove any scanner/site effects. After this, we feed the standardized data into the model, and interpret its results through the 80/20 split. Overall, I am excited to begin this process, however I must still get approval to these datasets in the first place. Until next time!