  Mammograms are commonly employed in the large scale screening of breast
cancer which is primarily characterized by the presence of malignant masses.
However, automated image-level detection of malignancy is a challenging task
given the small size of the mass regions and difficulty in discriminating
between malignant, benign mass and healthy dense fibro-glandular tissue. To
address these issues, we explore a two-stage Multiple Instance Learning (MIL)
framework. A Convolutional Neural Network (CNN) is trained in the first stage
to extract local candidate patches in the mammograms that may contain either a
benign or malignant mass. The second stage employs a MIL strategy for an image
level benign vs. malignant classification. A global image-level feature is
computed as a weighted average of patch-level features learned using a CNN. Our
method performed well on the task of localization of masses with an average
Precision/Recall of 0.76/0.80 and acheived an average AUC of 0.91 on the
imagelevel classification task using a five-fold cross-validation on the
INbreast dataset. Restricting the MIL only to the candidate patches extracted
in Stage 1 led to a significant improvement in classification performance in
comparison to a dense extraction of patches from the entire mammogram.
