Repository for SAMMY, a CNN Based Model to Predict Unlabeled Mammography Metadata (Left/Right, MLO/CC)

SAMMY (Small Automated Mammography Metadata Yielder)

Utils Folder

data_tools.py

Contains cbis_ddsm and inbreast classes for loading and handling the datasets

featurizer.py

Contains the functions for featurizing mammographys for feature based prediction

models.py

Contains class to test models on data (ResNet50, RandomForest, SmallCNN (SAMMY))

Settings.py

Contains settings for image compression and dataset path management

Tests Folder

random_forest.py

Runs a grid search of random forests on the cbis_ddsm dataset and validates the top models with inbreast, top model 87% on inbreast

small_cnn.py (SAMMY)

Trains a cnn on the cbis_ddsm dataset and validates on inbreast, 99% accuracy on inbreast

Datasets Used:

Sawyer-Lee, R., Gimenez, F., Hoogi, A., & Rubin, D. (2016). Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.7O02S9CY

Moreira, I. C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M. J., & Cardoso, J. S. (2012). INbreast: Toward a full-field digital mammographic database. Academic Radiology, 19(2), 236–248. https://doi.org/10.1016/j.acra.2011.09.014