In this page I have given sources of various data which was used in the assignments of three courses under AI in Medicine Specialisation.
I have successfully completed the courses and eventually the specialisation.
Here are the links (as on 17th July 2020):
AI for Medical Diagnosis - Assignments data
AI for Medical Prognosis - Assignments data
AI for Medical Treatment - Assignment data
Feel free to write in case of any suggestions or query through contact form page.
Note:
Please note that the above files do not contain the assignment ipynb files. Also the directory where you extract the above data should contain the assignment ipynb files.
The above files also contain the images used in nih & nih_new sub-folder used in the assignment.
I have been able to successfully complete all the assignments using above data in Google Colab also.
Except few sections (not part of assignments but included in the assignment sheets e.g. where R is required to run random forest, pre-trained models are used for analysis etc.) everything else smoothly runs on Google Colab (after installing few missing packages which can be easily installed in Google Colab).
If one is running the assignment ipynb sheet on Google Colab then need to install few packages in the Colab. For AI for Diagnosis course one may need to install tensorflow==1.14, for AI for Prognosis one may need to install shap & lifelines, for AI for Medical Treatment one may need to install shap, lifelines, transformers, stanfordnlp, bllipparser, svgling, negbio using !pip install e.g. !pip install lifelines
Also in Google Colab one may need to execute 'import drive' as well 'import os' commands in order to refer to files in the Google drive. It is better to upload the files in Google drive and access it from there in Google Colab during execution to save time & space.