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- Train_ResNet.py: this is the first script to execute if you want to train the CNN ResNet model from scratch. You may train the network four times, one per projection. You can use your own dataset as training set. It should consist of at least about 1000 images and it 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). In "CC_R_model" directory we saved the output of a training as an example.
- Prediction._ResNet.py: this is the script to test the saved trained model on new images. It is suggested to use your own test set also for testing. The test set of images should be organized in folders as the training set.. If you don't have enough images to train the model but you just want to test it, you can use a pre-trained model ('weights-improvement-46-0.80.h5') saved in the folder "CC_R_model".
- Figurefigure_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 script "Prediction_ResNet.py" and containing the prediction outcomes. We uploaded files "predictions_mlor.txt, predictions_ccr.txt, predictions_mlol.txt, predictions_ccl.txt" files in folder "Prediction", obtained from a pre-trained 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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