Combining deep learning and structured prediction for segmenting masses in mammograms

The segmentation of masses from mammogram is a challenging problem because of their variability in terms of shape, appearance and size, and the low signal-to-noise ratio of their appearance. We address this problem with structured output prediction models that use potential functions based on deep convolution neural network (CNN) and deep belief network (DBN). The two types of structured output prediction models that we study in this work are the conditional random field (CRF) and structured support vector machines (SSVM). The label inference for CRF is based on tree re-weighted belief propagation (TRW) and training is achieved with the truncated fitting algorithm; whilst for the SSVM model, inference is based upon graph cuts and training depends on a max-margin optimization. We compare the results produced by our proposed models using the publicly available mammogram datasets DDSM-BCRP and INbreast, where the main conclusion is that both models produce results of similar accuracy, but the CRF model shows faster training and inference. Finally, when compared to the current state of the art in both datasets, the proposed CRF and SSVM models show superior segmentation accuracy