Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks
This work addresses breast cancer detection in mammography, offering an incremental improvement by integrating symmetry information into deep learning models.
The study tackled detecting malignant soft tissue lesions in mammography by adding symmetry and temporal context to a deep CNN, finding that the fusion architecture with symmetry information significantly increased performance, while temporal data provided only marginal gains.
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.