Suspicion Map: an unsupervised middleware for analyzing digital mammograms

Abstract

Identifying “suspicious” regions is an essential process for clinical assessment of digital mammograms in breast cancer screening. Traditional solutions attempt to model malignant lesions directly, necessitating segmentations/annotations for training machine learning models. In this paper, we present a novel approach to identify a suspicion map – a middleware preserving only the suspicious regions in digital mammograms to effectively narrow down the image search space. Our unsupervised method is implemented by modeling normal breast tissue and subsequently identifying tissue abnormal to the model as suspicious. Our method consists of three main components: superpixel-based breast tissue patch generation, deep learning-based feature extraction from normal tissue patches, and breast density-guided one-class modeling of normal patches using the extracted features. Our machine learning approach is able to safely eliminate normal regions of tissue in a digital mammogram. Our normal tissue models were learned from 2,602 normal mammogram images and tested on 180 images (including 90 normal screening mammogram images and an independent set of 90 mammogram images with breast cancer diagnoses). Initial experiments showed that our proposed method can eliminate 97% of normal regions in the normal testing mammograms and 96% of normal regions in the malignant testing mammograms. Our method, based on modeling normal breast tissue, provides a novel and unsupervised scheme to more effectively analyze digital mammogram images towards identifying suspicious regions, and has the potential to benefit a variety of downstream applications for computeraided detection, diagnosis, and triage of breast cancer in mammogram images.