Advanced deep learning (DL) algorithms may predict the patient's risk of developing breast cancer based on the Breast Imaging Reporting and Data System (BI-RADS) and density standards. Recent studies have suggested that the combination of multi-view analysis improved the overall breast exam classification. In this paper, we propose a novel multi-view DL approach for BI-RADS and density assessment of mammograms. The proposed approach first deploys deep convolutional networks for feature extraction on each view separately. The extracted features are then stacked and fed into a Light Gradient Boosting Machine (LightGBM) classifier to predict BI-RADS and density scores. We conduct extensive experiments on both the internal mammography dataset and the public dataset Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed approach outperforms the single-view classification approach on two benchmark datasets by huge F1-score margins (+5% on the internal dataset and +10% on the DDSM dataset). These results highlight the vital role of combining multi-view information to improve the performance of breast cancer risk prediction.
A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms
Beijing Institute of Technology（北京理工大学）；Université 8 Mai 1945 Guelma（盖勒马8 Mai 1945大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院肿瘤医院国家癌症中心国家肿瘤临床研究中心）
Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms
Netherlands Cancer Institute (NKI)（荷兰癌症研究所）；GROW School for Oncology and Development Biology, Maastricht University（马斯特里赫特大学GROW肿瘤学与发育生物学学院）；Macao Polytechnic University（澳门理工大学）；Radboud University Medical Centre（拉德堡德大学医学中心）
RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement
Monash University Malaysia（莫纳什大学马来西亚分校）；The University of Oklahoma（俄克拉荷马大学）；Karolinska Institute（卡罗林斯卡学院）；Karolinska University Hospital（卡罗林斯卡大学医院）；University of Malaya（马来亚大学）；The University of Melbourne（墨尔本大学）
Human Gist Processing Augments Deep Learning Breast Cancer Risk Assessment
The Ohio State University（俄亥俄州立大学）；IBM Watson Health（IBM沃森健康）；University of York（约克大学）；Stanford University（斯坦福大学）；Harvard University（哈佛大学）；Brigham & Women’s Hospital（布莱根妇女医院）
A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusion
Department of Mathematics and Statistics, Qatar University（数学与统计学系，卡塔尔大学）；Department of Computer Science and Engineering, Qatar University（计算机科学与工程系，卡塔尔大学）；Department of Electrical Engineering, Qatar University（电气工程系，卡塔尔大学）；Graduate School of Information Science, University of Hyogo（信息科学研究生院，广岛大学）；Advanced Medical Engineering Research Institute, University of Hyogo（先进医疗工程研究所，广岛大学）
LUMINA: A Multi-Vendor Mammography Benchmark with Energy Harmonization Protocol
Department of Radiology, Northwestern University（北western大学放射科）；Department of Radiation Oncology, University of Health Sciences Prof. Dr. Cemil Tascioglu City Hospital（健康科学大学教授Dr. Cemil Tascioglu医院放射肿瘤科）；Department of Radiology, Istanbul University（伊斯坦布尔大学放射科）
Mammographic Breast Positioning Assessment via Deep Learning
Istanbul Technical University（伊斯坦布尔理工大学）；Acibadem Healthcare Group（阿西巴德姆医疗集团）；Acibadem Mehmet Ali Aydinlar University（阿西巴德姆穆罕默德·阿里·艾丁拉尔大学）；Sisli Hamidiye Etfal Training and Research Hospital（锡什利哈米迪耶埃特法尔培训与研究医院）
