Detecting Cardiovascular Disease from Mammograms with Deep Learning

By Juan Wang, Huanjun Ding, Fatemeh Azamian Bidgoli, Brian Zhou, C. Iribarren, Sabee Y. Molloi, Pierre Baldi. 2017

199 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gregor Urban
2. F. Jalali
3. W. Karnes
4. Priya Tripathi
5. T. Alkayali
6. Mohit Mittal
7. Yuan Xue
8. Ala Al-Fuqaha

In Radiology, Nuclear Medicine and Imaging

1. Ala Al-Fuqaha
2. Pierre Baldi
3. Li-Jia Li
4. Xinggang Wang
5. Gregor Urban

Citations in the corpus

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