Automated pre-selection of mammograms without abnormalities using deep learning

J. Teuwen, M. Kallenberg, A. Gubern-Merida, A. Rodriguez-Ruiz, N. Karssemeijer and R. Mann

Annual Meeting of the Radiological Society of North America 2017.

PURPOSE

In this study we evaluated the potential of a computer system to select exams with low likelihood of

containing cancer.

METHOD AND MATERIALS

We collected a representative set of 1649 referrals with different screening outcome from the Dutch

breast cancer screening. The dataset comprised 489 true positives (TP) exams and 1160 false

positive (FP) exams. In addition, we collected 1000 true negative (TN) exams from the same

screening population. All exams were automatically analyzed with Transpara v1.2.0 (ScreenPoint

Medical, Nijmegen, The Netherlands). Transpara uses deep learning algorithms to, based on

soft-tissue lesions and calcifications findings, categorize every mammogram on a 10-point scale. This

computerized score represents the likelihood that a cancer is present in the exam at hand, where 10

represents the highest likelihood that a cancer is present. It is defined in such a way that, in a

screening setting, the number of mammograms in each category is roughly equal.

In this study, we determined the distribution of the computerized cancer likelihood scores for the TP