A Multi-Task Learning Approach for Segmentation of Breast Arterial Calcifications in Screening Mammograms.

Screening mammogram is a standard and cost-efficient imaging procedure to measure breast cancer risk among 45+ year old women. Quantifying breast arterial calcification (BAC) from screening mammograms is a non-invasive and cost-efficient approach to assess the future risk of adverse cardiovascular events among women, such as heart attack and stroke. However, segmentation of breast arterial calcification is an involved task and poses several technical challenges such as extremely small BAC finding, low breast arteries to breast area ratio in the mammogram images, tissue features such as breast folds and heterogeneous density, have very similar imaging appearance. In this work, we aim to address the shortcomings of existing SOTA methods, e.g., SCUNet, and analyze the comparative performance. Given the fact that we will not be able to simply resize mammogram to preserve the microscopic BAC details, we adopted a patch-based methodology for segmentation using the original resolution which may hinder the model understanding of whole mammogram. We propose a multi-task learning approach for patch-based BAC segmentation by adding an auxiliary task of patch position prediction which forces the model to learn breast anatomy to comprehend the locations where BAC will not occur, such as breast boundary. The proposed method achieves state-of-the-art performance compared to the baselines. To demonstrate the utility, we also validate our method on external data and provide survival analysis for adverse cardiac events based on difference in BAC score and provide a comparison with coronary calcium score (CAC).