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Some examples of these preprocessed mammographic images are available as test images on GitHub repository:
The aim of this tutorial is to share our knowledge to allow you to use your own dataset, which could be fundamental to evaluate the robustness of the model.
After training, testing and evaluating the classification performance of the CNN through the figures of merit of accuracy, recall and precision, the second step is to check if the network is reliable and understand the reasons behind its predictions, as being a deep neural network remains , they remain quite unclear. This means that we want to verify if the classification is made by “looking at” what we expected, i.e. the dense regions in the mammogram. This analysis of explainability could be implemented through an off-line (applied after training the model, without altering its architecture) visualization technique. This technique aims to identify which discriminative pixels in the image influence the final prediction.
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