There is provided a method, comprising: segmenting fibroglandular tissue of a 2D mammographic image of a breast, extracting regions within the segmented fibroglandular tissue and within a boundary portion between the segmented fibroglandular tissue and non-fibroglandular tissue, computing representations for each RoI by a pre-trained deep neural network, training a classifier on the representations to compute a probability score of architectural distortion for each RoI, clustering RoIs defined as positive for architectural distortion using a mean-shift method and providing an indication of the probability of the presence of architectural distortion around a cluster based on the probability distribution of cluster RoI members, removing small clusters having fewer RoI members than a small number threshold, classifying the image as positive for the indication of architectural distortion when at least one cluster remains, or classifying the image as negative for the indication of architectural distortion when no cluster remains.
