  The ADMANI datasets (annotated digital mammograms and associated non-image
datasets) from the Transforming Breast Cancer Screening with AI programme
(BRAIx) run by BreastScreen Victoria in Australia are multi-centre, large
scale, clinically curated, real-world databases. The datasets are expected to
aid in the development of clinically relevant Artificial Intelligence (AI)
algorithms for breast cancer detection, early diagnosis, and other
applications. To ensure high data quality, technical outliers must be removed
before any downstream algorithm development. As a first step, we randomly
select 30,000 individual mammograms and use Convolutional Variational
Autoencoder (CVAE), a deep generative neural network, to detect outliers. CVAE
is expected to detect all sorts of outliers, although its detection performance
differs among different types of outliers. Traditional image processing
techniques such as erosion and pectoral muscle analysis can compensate for the
poor performance of CVAE in certain outlier types. We identify seven types of
technical outliers: implant, pacemaker, cardiac loop recorder, improper
radiography, atypical lesion/calcification, incorrect exposure parameter and
improper placement. The outlier recall rate for the test set is 61% if CVAE,
erosion and pectoral muscle analysis each select the top 1% images ranked in
ascending or descending order according to image outlier score under each
detection method, and 83% if each selects the top 5% images. This study offers
an overview of technical outliers in the ADMANI dataset and suggests future
directions to improve outlier detection effectiveness.
