Meta-repository of screening mammography classifiers
NYU Langone Health（纽约大学朗格尼健康中心）；Jagiellonian University（雅盖隆大学）；NYU Abu Dhabi（纽约大学阿布扎比分校）；New York University（纽约大学）；Courant Institute, New York University（纽约大学库朗研究所）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）
Benjamin Stadnick, Jan Witowski, Vishwaesh Rajiv, Jakub Chłędowski, Farah E. Shamout, Kyunghyun Cho, Krzysztof J. Geras
Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer diagnosis and reduce unnecessary workup. As the number of proposed models and their complexity grows, it is becoming increasingly difficult to re-implement them. To enable reproducibility of research and to enable comparison between different methods, we release a meta-repository containing models for classification of screening mammograms. This meta-repository creates a framework that enables the evaluation of AI models on any screening mammography data set. At its inception, our meta-repository contains five state-of-the-art models with open-source implementations and cross-platform compatibility. We compare their performance on seven international data sets. Our framework has a flexible design that can be generalized to other medical image analysis tasks. The meta-repository is available at https://www.github.com/nyukat/mammography_metarepository.
Full Field Digital Mammography Dataset from a Population Screening Program
Faculty of Medicine, Memorial University of Newfoundland（医学学院，纪念大学）；Department of Computer Science, Faculty of Science, Memorial University of Newfoundland（计算机科学系，科学学院，纪念大学）；Cancer Care, Newfoundland and Labrador Health Services（癌症护理，新斯科舍省健康服务）
A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study
Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine（NYU Grossman医学院Vilcek研究生生物医学科学研究所）；NYU Grossman School of Medicine（NYU Grossman医学院）；Center for Data Science, New York University（纽约大学数据科学中心）；Emory University（埃默里大学）；Visage Imaging, Inc.（Visage成像公司）
Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach
DeepHealth, Inc.（深健公司）；Rhode Island Hospital（罗德岛医院）；Brown University（布朗大学）；Henan Provincial People’s Hospital（河南省人民医院）；Medford Radiology Group（梅德福放射学集团）；University of Massachusetts Medical School（马萨诸塞大学医学院）
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Center for Data Science, New York University（纽约大学数据科学中心）；Department of Computer Science, Courant Institute, New York University（纽约大学库朗研究所计算机科学系）；Department of Radiology, NYU School of Medicine（纽约大学医学院放射科）；Perlmutter Cancer Center, NYU Langone Health（纽约大学朗格尼健康系统珀尔马特癌症中心）；Center for Advanced Imaging Innovation and Research, NYU Langone Health（纽约大学朗格尼健康系统先进成像创新与研究中心）；CIFAR Associate Fellow（CIFAR研究员（注：CIFAR是加拿大英属哥伦比亚大学的一个研究机构，但根据提取规则，此处仅提取“CIFAR”相关部分，严格按英文保留））
Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography
Kennesaw State University（肯尼索州立大学）；Emory University（埃默里大学）；Washington University in St. Louis School of Medicine（圣路易斯华盛顿大学医学院）；Mayo Clinic Arizona（梅奥诊所亚利桑那分院）；Arizona State University（亚利桑那州立大学）；York University（约克大学）
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）
Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis
NYU Center for Data Science（纽约大学数据科学中心）；Courant Institute of Mathematical Sciences, NYU（纽约大学库朗数学科学研究所）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Jagiellonian University（雅盖隆大学）
Automatic Application Level Set Approach in Detection Calcifications in Mammographic Image
National School of Medicine of Sfax（斯法克斯医学院）；CES-Computer, Electronic And Smart engineering systems design Laboratory（计算机、电子与智能工程系统设计实验室）；National School of Engineers of Sfax（斯法克斯工程师学院）
乳腺癌被认为是一个主要的健康问题，是世界上妇女死亡的最大原因。因此，在这十年中，乳腺癌是第二种最常见的癌症，就出现频率而言，也是第五种最常见的癌症相关死亡原因。为了减少放射科医师的工作量，已经提出了各种CAD系统;计算机辅助诊断（CADi）和计算机辅助检测（CADe）。在本文中，我们感兴趣的CADe工具，以帮助放射科医生检测癌症。建议的CADe是基于一个三步的工作流程，即检测，分析和分类。研究了基于边缘和区域准则的水平集方法的感兴趣区域自动检测问题。这种方法从放射科医生那里得到了良好的视觉信息。在此基础上，采用基于纹理特征的图像特征提取和基于多层感知（MLP）和k-近邻（KNN）的图像矢量分类方法，对图像进行了ACR（American College of Radiology）分类。此外，我们使用数字数据库筛选乳腺X射线摄影（DDSM）的实验和这些结果的准确性在60%和70%之间变化是可以接受的，必须加以改进，以帮助放射科医生。
Three Applications of Conformal Prediction for Rating Breast Density in Mammography
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（马萨诸塞州总医院阿西诺拉·A·马丁诺斯生物医学成像中心）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；University of Colorado School of Medicine（科罗拉多大学医学院）；Harvard Medical School（哈佛医学院）
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）
Deep Learning-Based Breast Cancer Detection in Mammography: A Multi-Center Validation Study in Thai Population
Perceptra Co., Ltd（Perceptra公司）；Institute of Field Robotics（现场机器人研究所）；King Mongkut’s University of Technology Thonburi（技术师范大学）；Mahidol University（玛希多大学）；Siriraj Hospital（Siriraj医院）；Khonkaen University（孔卡恩大学）
Detecting and Monitoring Bias for Subgroups in Breast Cancer Detection AI
University of Maryland（马里兰大学）；University of Maryland School of Medicine（马里兰大学医学院）；University of Maryland Institute for Advanced Computer Studies（马里兰大学高级计算机研究所）
本文评估乳腺癌检测 AI 在 EMBED 与 RSNA 2022 数据集上六类子群体的性能差异，并引入随时间漂移监测与告警机制，以发现偏见并保障跨人群有效性。
Towards order of magnitude X-ray dose reduction in breast cancer imaging using phase contrast and deep denoising
The University of Melbourne（墨尔本大学）；The University of Sydney（悉尼大学）；Eastern Health（东部健康）；Monash Health（莫纳什健康）；Monash University（莫纳什大学）；Australian Synchrotron, ANSTO（澳大利亚同步辐射中心，ANSTO）；Commonwealth Scientific and Industrial Research Organisation（澳大利亚联邦科学与工业研究组织）
Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms
Center for Data Science, New York University（纽约大学数据科学中心）；Department of Radiology, New York University School of Medicine（纽约大学医学院放射科）；Perlmutter Cancer Center, NYU Langone Health（纽约大学朗格尼医学中心珀尔马特癌症中心）；Center for Advanced Imaging Innovation and Research, NYU Langone Health（纽约大学朗格尼医学中心先进成像创新与研究中心）；Department of Computer Science, Courant Institute of Mathematical Sciences, New York University（纽约大学库朗数学科学研究所计算机科学系）
Globally-Aware Multiple Instance Classifier for Breast Cancer Screening
Center for Data Science, New York University（纽约大学数据科学中心）；Department of Radiology, New York University School of Medicine（纽约大学医学院放射学系）；Department of Computer Science, Courant Institute, New York University（纽约大学库朗研究所计算机科学系）；Facebook AI Research（脸书人工智能研究院）；CIFAR Azrieli Global Scholar（加拿大高等研究院阿兹里埃利全球学者）
Deep Learning Based Computer-Aided Systems for Breast Cancer Imaging : A Critical Review
Universidad Técnica Particular de Loja（洛哈技术大学）；Instituto de Instrumentacion para la Imagen Molecular I3M, Universitat Politécnica de Valencia（瓦伦西亚理工大学分子图像仪器研究所I3M）；University of Waterloo（滑铁卢大学）
