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Radiologists typically compare a patient's most recent breast cancerscreening exam to their previous ones in making informed diagnoses. To reflectthis practice, we propose new neural network models that compare pairs ofscreening mammograms from the same patient. We train and evaluate our proposedmodels on over 665,000 pairs of images (over 166,000 pairs of exams). Our bestmodel achieves an AUC of 0.866 in predicting malignancy in patients whounderwent breast cancer screening, reducing the error rate of the correspondingbaseline.

Screening Mammogram Classification with Prior Exams