Signals of global symmetry are important for abnormality detection in mammograms.

Purpose: Expert radiologists can detect the "gist of abnormal" in bilateral mammograms even three years prior to onset of cancer. However, their performance decreases if both breasts are not from the same woman, suggesting the ability to detect the abnormality is partly dependent on a global signal present across the two breasts. We aim to detect this implicitly perceived "symmetry" signal by examining its effect on a pre-trained mammography model.

Approach: A deep neural network (DNN) with four mammogram view inputs was developed to predict whether the mammograms come from one woman, or two different women as the first step in investigating the symmetry signal. Mammograms were balanced by size, age, density, and machine type. We then evaluated a cancer detection DNN's performance on mammograms from the same and different women. Finally, we used textural analysis methods to further explain the symmetry signal.

Results: The developed DNN can detect whether a set of mammograms come from the same or different woman with a base accuracy of 61%. Indeed, a DNN shown mammograms swapped either contralateral or abnormal with a normal mammogram from another woman, resulted in performance decreases. Findings indicate that abnormalities induce a disruption in global mammogram structure resulting in the break in the critical symmetry signal.

Conclusion: The global symmetry signal is a textural signal embedded in the parenchyma of bilateral mammograms, which can be extracted. The presence of abnormalities alters textural similarities between the left and right breasts and contributes to the "medical gist signal."