Towards Breast Cancer Diagnosis Using Multiple Mammography Views

This study introduces a novel computer aided diagnosis system to diagnose breast cancer using two mammography views as input i.e. MLO and CC. The pipeline consists of a convolutional autoencoder that is trained to extract features from different mammograms’ views, and one-dimensional convolutional neural network to classify the input embeddings into two classes i.e. benign or malignant. We compare the one-dimensional convolutional neural network classification results with a support vector machine trained on the same latent embeddings. We conclude that the combination of autoencoders and one-dimensional convolutional neural networks yields the best classification accuracy on the test set of the INbreast dataset.