Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers

Applying state-of-the-art machine learning techniques to medical images
requires a thorough selection and normalization of input data. One of such
steps in digital mammography screening for breast cancer is the labeling
and removal of special diagnostic views, in which diagnostic tools or
magnification are applied to assist in assessment of suspicious initial
findings. As a common task in medical informatics is prediction of disease
and its stage, these special diagnostic views, which are only enriched
among the cohort of diseased cases, will bias machine learning disease
predictions. In order to automate this process, here, we develop a machine
learning pipeline that utilizes both DICOM headers and images to predict
such views in an automatic manner, allowing for their removal and the
generation of unbiased datasets. We achieve AUC of 99.72% in predicting
special mammogram views when combining both types of models. Finally, we
apply these models to clean up a dataset of about 772,000 images with
expected sensitivity of 99.0%. The pipeline presented in this paper can be
applied to other datasets to obtain high-quality image sets suitable to
train algorithms for disease detection.