Welcome! This is the repository for all lectures, assignments, and datasets for Week 3 of the BeaverWorks Medlytics course for 2021. The slides are provided here for easy reference, but will be presented in lecture format. You should fork this repository at the beginning of the week and work on your own copy when completing notebooks and challenge projects.
Datasets Used in this Repo:
CBIS-DDSM: https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM Jpegs and labels were extracted from a tfrecords dataset curated by Eric Scuccimarra: https://www.kaggle.com/skooch Colorectal Histology Dataset: https://zenodo.org/record/53169#.W2hf_NJKh9M Kather JN, Weis CA, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Zollner F: Multi-class texture analysis in colorectal cancer histology (2016), Scientific Reports (in press)
- Contains Jupyter Notebook lessons where students will need to write their own code
- Contains Jupyter Notebook Solutions (will be uploaded after lesson notebooks are completed by students)
- Contains lesson notebooks (and solutions) on Texture Classification, Classification with Convulutional Neural Nets, Classification with Neural Nets, Classification using ImageNet, and Classification using VGG16. All notebooks made by Siddharth Samsi.
- Contains the Week 3 Image Analysis challenge (classifying mammogram images). Created by Thomas Possidente.
Mammogram Image Dataset: DDSM: http://marathon.csee.usf.edu/Mammography/Database.html