  Breast cancer remains a leading cause of cancer-related deaths among women
worldwide, with mammography screening as the most effective method for the
early detection. Ensuring proper positioning in mammography is critical, as
poor positioning can lead to diagnostic errors, increased patient stress, and
higher costs due to recalls. Despite advancements in deep learning (DL) for
breast cancer diagnostics, limited focus has been given to evaluating
mammography positioning. This paper introduces a novel DL methodology to
quantitatively assess mammogram positioning quality, specifically in
mediolateral oblique (MLO) views using attention and coordinate convolution
modules. Our method identifies key anatomical landmarks, such as the nipple and
pectoralis muscle, and automatically draws a posterior nipple line (PNL),
offering robust and inherently explainable alternative to well-known
classification and regression-based approaches. We compare the performance of
proposed methodology with various regression and classification-based models.
The CoordAtt UNet model achieved the highest accuracy of 88.63% $\pm$ 2.84 and
specificity of 90.25% $\pm$ 4.04, along with a noteworthy sensitivity of 86.04%
$\pm$ 3.41. In landmark detection, the same model also recorded the lowest mean
errors in key anatomical points and the smallest angular error of 2.42 degrees.
Our results indicate that models incorporating attention mechanisms and
CoordConv module increase the accuracy in classifying breast positioning
quality and detecting anatomical landmarks. Furthermore, we make the labels and
source codes available to the community to initiate an open research area for
mammography, accessible at https://github.com/tanyelai/deep-breast-positioning.
