|
Materials and Methods: Full-field digital screening mammograms acquired in our clinics were reviewed from 2006-2015. Interval cancers were matched to screening-detected cancers based on age, race, exam date, and time since last imaging examination. A deep learning architecture (ResNet50) was trained on this dataset with the goal to classify between interval and screen detected cancers. Network weights were initialized from ImageNet training and the final fully connected layers were retrained. Prediction loss, prediction accuracy, and ROC curves were calculated using this deep learning architecture and compared to predictions from conditional logistic regression using BI-RADS density. Results: 182 interval and 173 screening-detected cancers were found in our study group. The prediction accuracy improved from 63% using only BI-RADS density to 78% after including predictions from the deep learning model. The area under the ROC curve improved from 0.65 using only BI-RADS density to 0.84 after including the deep learning network as a predictor. Conclusions: We conclude that deep learning methods may be useful in identifying individuals at risk of interval cancer and that these methods can provide additional risk information not contained in breast density alone. |
ACCESS THE FULL ARTICLE
No SPIE Account? Create one
Cancer
Breast cancer
Mammography
Neural networks
Data modeling
Image classification
Breast
