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- Train_ResNet.py: this is the first script to train the CNN ResNet model. You may train the network four times, one per projection. Therefore, the training set of images should be divided in 4 different folders (CC_R, CC_L, MLO_L, MLO_R) and each folder divided into 4 sub-folders, one per class (A, B, C, D).
- predictionPrediction.ResNet.py: this is the script to test the saved trained model on new images. The test set of images should be organized in folders as the training set.
- figure_merit.ipynb: this is the script to obtain the metrics, the final figures of merit to evaluate the classification performance on the test set. You need as input the .txt files, obtained with the script prediction.py with the prediction outcomes. We uploaded files predictions_mlor.txt, predictions_ccr.txt, predictions_mlol.txt, predictions_ccl.txt obtained from a pre-trained and tested ResNet model to use them as examples.
- CAM_CC_R_A.ipynb: this is the script to obtain the Class Activation Maps. You can use the test images we uploaded as example in the folder “TestSet” in GitHub repo.
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