Detecting Asymmetric Patterns and Localizing Cancers on Mammograms

• Main Authors: Yuanfang Guan, Xueqing Wang, Hongyang Li, Zhenning Zhang, Xianghao Chen, Omer Siddiqui, Sara Nehring, Xiuzhen Huang
• Format: Article
• Language: English
• Published: Elsevier 2020-10-01
• Series: Patterns
• Subjects: breast cancer; digital mammography; asymmetry; deep learning; computer-assisted diagnosis
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666389920301409

Summary: One in eight women develops invasive breast cancer in her lifetime. The frontline protection against this disease is mammography. While computer-assisted diagnosis algorithms have made great progress in generating reliable global predictions, few focus on simultaneously producing regions of interest (ROIs) for biopsy. Can we combine ROI-oriented algorithms with global classification of cancer status, which simultaneously highlight suspicious regions and optimize classification performance? Can the asymmetry of breasts be adopted in deep learning for finding lesions and classifying cancers? We answer the above questions by building deep-learning networks that identify masses and microcalcifications in paired mammograms, exclude false positives, and stepwisely improve performance of the model with asymmetric information regarding the breasts. This method achieved a co-leading place in the Digital Mammography DREAM Challenge for predicting breast cancer. We highlight here the importance of this dual-purpose process that simultaneously provides the locations of potential lesions in mammograms. The Bigger Picture: Breast cancer affects one out of eight women in their lifetime. Given the importance of the need, in this work we present a region-of-interest-oriented deep-learning pipeline for detecting and locating breast cancers based on digital mammograms. It is a leading algorithm in the well-received Digital Mammography DREAM Challenge, in which computational methods were evaluated on large-scale, held-out testing sets of digital mammograms. This algorithm connects two aims: (1) determining whether a breast has cancer and (2) determining cancer-associated regions of interest. Particularly, we addressed the challenge of variation of mammogram images across different patients by pairing up the two opposite breasts to examine asymmetry, which substantially improved global classification as well as local lesion detection. We have dockerized this code, envisioning that it will be widely used in practice and as a future reference for digital mammography analysis.