Keywords: breast cancer screening, mammography, convolutional neural networks, breast cancer screening exam classification, utilizing prior exams
TL;DR: A comparison network utilizing prior exams improves breast cancer screening exam classification.
Abstract: Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses. To reflect this practice, we propose new neural network models that compare pairs of screening mammograms from the same patient. We train and evaluate our proposed models on over 665,000 pairs of images (over 166,000 pairs of exams). Our best model achieves an AUC of 0.866 in predicting malignancy in patients who underwent breast cancer screening, reducing the error rate of the corresponding baseline.
Code Of Conduct: I have read and accept the code of conduct.