Title: Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks,
Abstract: We investigate the addition of symmetry and temporal context information to a
deep Convolutional Neural Network (CNN) with the purpose of detecting malignant
soft tissue lesions in mammography. We employ a simple linear mapping that
takes the location of a mass candidate and maps it to either the contra-lateral
or prior mammogram and Regions Of Interest (ROI) are extracted around each
location. We subsequently explore two different architectures (1) a fusion
model employing two datastreams were both ROIs are fed to the network during
training and testing and (2) a stage-wise approach where a single ROI CNN is
trained on the primary image and subsequently used as feature extractor for
both primary and symmetrical or prior ROIs. A 'shallow' Gradient Boosted Tree
(GBT) classifier is then trained on the concatenation of these features and
used to classify the joint representation. Results shown a significant increase
in performance using the first architecture and symmetry information, but only
marginal gains in performance using temporal data and the other setting. We
feel results are promising and can greatly be improved when more temporal data
becomes available.