Mammogram is the most effective imaging modality for the mass lesion detection of breast cancer at the early stage. The information from the two paired views (i.e., medio-lateral oblique and cranio-caudal) are highly relational and complementary, and this is crucial for doctors' decisions in clinical practice. However, existing mass detection methods do not consider jointly learning effective features from the two relational views. To address this issue, this paper proposes a novel mammogram mass detection framework, termed Cross-View Relation Region-based Convolutional Neural Networks (CVR-RCNN). The proposed CVR-RCNN is expected to capture the latent relation information between the corresponding mass region of interests (ROIs) from the two paired views. Evaluations on a new large-scale private dataset and a public mammogram dataset show that the proposed CVR-RCNN outperforms existing state-of-the-art mass detection methods. Meanwhile, our experimental results suggest that incorporating the relation information across two views helps to train a superior detection model, which is a promising avenue for mammogram mass detection.
In-context Cross-Density Adaptation on Noisy Mammogram Abnormalities Detection
VinBrain JSC（VinBrain股份公司）；VinBigData（Vin大数据公司）；University of Education（教育大学）；University of Medicine and Pharmacy（医药大学）；Vietnamese-German University（越德大学）
BRAIxDet: Learning to Detect Malignant Breast Lesion with Incomplete Annotations
Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；St Vincent’s Institute of Medical Research（圣文森特医学研究所）；St Vincent’s Hospital Melbourne（墨尔本圣文森特医院）；Melbourne Integrative Genomics, The University of Melbourne（墨尔本大学墨尔本整合基因组学研究所）
Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation
The University of Adelaide（阿德莱德大学）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；St Vincent’s Institute of Medical Research（圣文森特医学研究所）；St Vincent’s Hospital Melbourne（墨尔本圣文森特医院）
Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection
Deepwise Healthcare（深睿医疗）；Peking University（北京大学）；Advanced Institute of Information Technology, Peking University（北京大学先进信息技术研究院）；The University of Hong Kong（香港大学）
Delving into Ipsilateral Mammogram Assessment under Multi-View Network
National Cheng Kung University（台湾成功大学）；Ho Chi Minh City University of Science（胡志明市科学大学）；Military Hospital 175（175军医院）；Eastern International University（东方国际大学）；Ho Chi Minh City International University（胡志明市国际大学）
TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detection
VinUni-Illinois Smart Health Center, VinUniversity（VinUni-Illinois智慧健康中心，VinUniversity）；College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）
Expert identification of visual primitives used by CNNs during mammogram classification
CSAIL, MIT（麻省理工学院计算机科学与人工智能实验室）；Harvard University（哈佛大学）；Department of Radiological Sciences, UCLA（加州大学洛杉矶分校放射科学系）；Beth Israel Deaconess Medical Center（贝丝以色列女执事医疗中心）；Massachusetts General Hospital（麻省总医院）；Microsoft Research New England（微软新英格兰研究院）
Simultaneous Dual-View Mammogram Synthesis Using Denoising Diffusion Probabilistic Models
Tecnologico de Monterrey, School of Engineering and Sciences（蒙特雷科技大学工程与科学学院）；School of Engineering, Pontificia Universidad Católica de Chile（智利天主教大学工程学院）；Tecnologico de Monterrey, School of Medicine and Health Sciences（蒙特雷科技大学医学与健康科学学院）
MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screening
University of Cambridge（剑桥大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong Polytechnic University（香港理工大学）；Barts Health NHS Trust（巴茨健康国民保健服务信托）；The Royal London Hospital（皇家伦敦医院）
Can a Second-View Image Be a Language? Geometric and Semantic Cross-Modal Reasoning for X-ray Prohibited Item Detection
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学学院）；State Key Laboratory of Complex & Critical Software Environment, Beihang University（北航复杂与关键软件环境国家重点实验室）
Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN
Technical University of Munich（慕尼黑工业大学）；University of the Bundeswehr Munich（慕尼黑联邦国防军大学）；China University of Mining and Technology（中国矿业大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Tongji University（同济大学）
Dual-view Curricular Optimal Transport for Cross-lingual Cross-modal Retrieval
Zhejiang Gongshang University（浙江工商大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Alibaba Group（阿里巴巴集团）；Binjiang Institute of Zhejiang University（浙江大学滨江研究院）；Hefei University of Technology（合肥工业大学）
Integrating AI for Human-Centric Breast Cancer Diagnostics: A Multi-Scale and Multi-View Swin Transformer Framework
Concordia Institute for Information Systems Engineering (CIISE)（康科迪亚信息系统工程研究所）；Concordia University（康科迪亚大学）；Thomas Jefferson University Hospital（泰勒斯·杰弗里斯大学医院）；College of Computing & Informatics（计算与信息学院）；Drexel University（德雷塞尔大学）
Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis
National Cheng Kung University（成功大学）；Saigon Precision Medicine Research Center (SAIGONMEC)（西贡精准医学研究中心）；Taipei Medical University（台北医学大学）；Ho Chi Minh City International University（胡志明市国际大学）；Chonnam National University（全南国立大学）；Vietnamese-German University（越德大学）；Research Center for Artificial Intelligence in Medicine, Taipei Medical University（台北医学大学医学人工智能研究中心）
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning
Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Jiao Tong University（上海交通大学）；The University of Hong Kong（香港大学）；Yale University（耶鲁大学）；ShanghaiTech University（上海科技大学）
Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network
Escola Politécnica, Universidade de São Paulo（圣保罗大学理工学院）；Instituto do Câncer do Estado de São Paulo（圣保罗州癌症研究所）；Faculdade de Medicina, Universidade de São Paulo（圣保罗大学医学院）
Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Institute for Infocomm Research, A*STAR（新加坡科技研究局信息通信研究院）
More Than Where You Are: Learning Semantics, Structure, and Geometry from Cross-View Localization
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Aerospace Control Center（北京航天飞行控制中心）
