1. A method for using a trained statistical classifier for detecting an indication of architectural distortion in a mammographic image, comprising: receiving a two dimensional (2D) mammographic image of a breast; segmenting fibroglandular tissue of the breast to create a segmented fibroglandular tissue region; extracting a plurality of regions within the segmented fibroglandular tissue region and within a boundary portion between the segmented fibroglandular tissue and non-fibroglandular tissue; computing representations for each region of interest (RoI) by a pre-trained deep neural network; training a classifier on the computed representations to compute a respective probability score of architectural distortion associated with each RoI; defining each RoI having the probability score above a threshold as positive for architectural distortion; clustering the RoIs defined as positive using a mean-shift method and providing an indication of the probability of the presence of architectural distortion around a cluster based on a probability distribution of cluster RoI members; removing small clusters created by the clustering of the RoI according to a small number threshold, wherein clusters having fewer RoI members than the small number threshold are removed; classifying the image as positive for the indication of architectural distortion when at least one cluster remains after the removing, or classifying the image as negative for the indication of architectural distortion when no cluster remains after the removing; and outputting a classification of the image.