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Convolutional Neural Networks (CNN) have had a huge success in many areas of computer vision and medical image analysis. However, there is still an immense potential for performance improvement in mammogram breast cancer detection Computer-Aided Detection (CAD) systems by integrating all the information that the radiologist utilizes, such as symmetry and temporal data. In this work, we proposed a patch based multi-input CNN that learns symmetrical difference to detect breast masses. The network was trained on a large-scale dataset of 28294 mammogram images. The performance was compared to a baseline architecture without symmetry context using Area Under the ROC Curve (AUC) and Competition Performance Metric (CPM). At candidate level, AUC value of 0.933 with 95% confidence interval of [0.920, 0.954] was obtained when symmetry information is incorporated in comparison with baseline architecture which yielded AUC value of 0.929 with [0.919, 0.947] confidence interval. By incorporating symmetrical information, although there was no a significant candidate level performance again (p = 0.111), we have found a compelling result at exam level with CPM value of 0.733 (p = 0.001). We believe that including temporal data, and adding benign class to the dataset could improve the detection performance.
Breast cancer is one of the leading fatal disease worldwide with high risk control if early discovered. Conventional method for breast screening is x-ray mammography, which is known to be challenging for early detection of cancer lesions. The dense b
Purpose: To determine whether deep learning models can distinguish between breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and methods: In this institutional review board-approved si
Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018. To achieve earlier breast cancer detection, screening x-ray mammography is recommended by health organizations worldwide and has been estimated to decrease brea
The computer-aided detection (CADe) systems are developed to assist pathologists in slide assessment, increasing diagnosis efficiency and reducing missing inspections. Many studies have shown such a CADe system with deep learning approaches outperfor
Breast cancer is one of the leading causes of mortality in women. Early detection and treatment are imperative for improving survival rates, which have steadily increased in recent years as a result of more sophisticated computer-aided-diagnosis (CAD