Abstract：
:Coronary artery disease is a major cause of death in women. Breast arterial calcifications (BACs), detected inmammograms, can be useful riskmarkers associated with the disease. We investigate the feasibility of automated and accurate detection ofBACsinmammograms for risk assessment of coronary artery disease. We develop a 12-layer convolutional neural network to discriminate BAC from non-BAC and apply a pixelwise, patch-based procedure for BAC detection. To assess the performance of the system, we conduct a reader study to provide ground-truth information using the consensus of human expert radiologists. We evaluate the performance using a set of 840 full-field digital mammograms from 210 cases, using both free-responsereceiveroperatingcharacteristic (FROC) analysis and calcium mass quantification analysis. The FROC analysis shows that the deep learning approach achieves a level of detection similar to the human experts. The calcium mass quantification analysis shows that the inferred calcium mass is close to the ground truth, with a linear regression between them yielding a coefficient of determination of 96.24%. Taken together, these results suggest that deep learning can be used effectively to develop an automated system for BAC detection inmammograms to help identify and assess patients with cardiovascular risks.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Wang J,Ding H,Bidgoli FA,Zhou B,Iribarren C,Molloi S,Baldi Pdoi
10.1109/TMI.2017.2655486subject
Has Abstractpub_date
2017-05-01 00:00:00pages
1172-1181issue
5eissn
0278-0062issn
1558-254Xjournal_volume
36pub_type
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