  Screening mammograms are a routine imaging exam performed to detect breast
cancer in its early stages to reduce morbidity and mortality attributed to this
disease. In order to maximize the efficacy of breast cancer screening programs,
proper mammographic positioning is paramount. Proper positioning ensures
adequate visualization of breast tissue and is necessary for effective breast
cancer detection. Therefore, breast-imaging radiologists must assess each
mammogram for the adequacy of positioning before providing a final
interpretation of the examination; this often necessitates return patient
visits for additional imaging. In this paper, we propose a deep
learning-algorithm method that mimics and automates this decision-making
process to identify poorly positioned mammograms. Our objective for this
algorithm is to assist mammography technologists in recognizing inadequately
positioned mammograms real-time, improve the quality of mammographic
positioning and performance, and ultimately reducing repeat visits for patients
with initially inadequate imaging. The proposed model showed a true positive
rate for detecting correct positioning of 91.35% in the mediolateral oblique
view and 95.11% in the craniocaudal view. In addition to these results, we also
present an automatically generated report which can aid the mammography
technologist in taking corrective measures during the patient visit.
