Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis
approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features
directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for
learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided
diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed
unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling.
We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities
(739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated
by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006)
on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of
binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving
dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in
2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx
schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an
ultrasound image set (1125 cases).