Localisation of Mammographic masses by Greedy Backtracking of
  Activations in the Stacked Auto-Encoders
Shamna Pootheri Govindan V K
Mammographic image analysis requires accurate localisation of salient
mammographic masses. In mammographic computer-aided diagnosis, mass or Region
of Interest (ROI) is often marked by physicians and features are extracted from
the marked ROI. In this paper, we present a novel mammographic mass
localisation framework, based on the maximal class activations of the stacked
auto-encoders. We hypothesize that the image regions activating abnormal
classes in mammographic images will be the breast masses which causes the
anomaly. The experiment is conducted using randomly selected 200 mammographic
images (100 normal and 100 abnormal) from IRMA mammographic dataset. Abnormal
mass regions marked by an expert radiologist are used as the ground truth. The
proposed method outperforms existing Deep Convolutional Neural Network (DCNN)
based techniques in terms of salient region detection accuracy. The proposed
greedy backtracking method is more efficient and does not require a vast number
of labelled training images as in DCNN based method. Such automatic
localisation method will assist physicians to make accurate decisions on biopsy
recommendations and treatment evaluations.