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

A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms