Breast cancer screening, primarily conducted through mammography, is often supplemented with ultrasound for women with dense breast tissue. However, existing deep learning models analyze each modality independently, missing opportunities to integrate information across imaging modalities and time. In this study, we present Multi-modal Transformer (MMT), a neural network that utilizes mammography and ultrasound synergistically, to identify patients who currently have cancer and estimate the risk of future cancer for patients who are currently cancer-free. MMT aggregates multi-modal data through self-attention and tracks temporal tissue changes by comparing current exams to prior imaging. Trained on 1.3 million exams, MMT achieves an AUROC of 0.943 in detecting existing cancers, surpassing strong uni-modal baselines. For 5-year risk prediction, MMT attains an AUROC of 0.826, outperforming prior mammography-based risk models. Our research highlights the value of multi-modal and longitudinal imaging in cancer diagnosis and risk stratification.
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）
Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging
Athinoula A. Martinos Center for Biomedical Imaging, Mass General Brigham and Harvard Medical School（Athinoula A. Martinos 生物医学成像中心，马萨诸塞总医院与哈佛医学院）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin（柏林夏里特医学院，自由大学和洪堡大学的成员）；Department of Radiology, University of Wisconsin School of Medicine and Public Health（威斯康星大学医学与公共卫生学院放射科）
Pretrained Vision Models for Predicting High-Risk Breast Cancer Stage
McGill University（麦吉尔大学）；Mila Quebec AI Institute（米拉魁北克人工智能研究所）；Lelapa AI（莱拉帕人工智能）；Masakhane Research Foundation（马萨卡内研究基金会）；Drexel University（德雷塞尔大学）；University Of Maryland Baltimore County（马里兰大学巴尔的摩县分校）
癌症日益成为全球性的健康问题。仅次于心血管疾病，癌症是世界上第二大死亡原因，每年有数百万人死于癌症。根据世界卫生组织（WHO）的报告，截至2020年底，超过780万女性被诊断患有乳腺癌，使其成为世界上最流行的癌症。在本文中，使用数字病理学（乳腺活检）图像的Nightingale Open Science数据集，我们利用预先训练的计算机视觉模型的功能进行乳腺癌分期预测任务。虽然个别模型实现体面的表现，我们发现，一个合奏模型的预测更有效，并提供了一个成功的解决方案\footnote{https://www.nightingalescience.org/updates/hbc1-results}。我们还提供了结果的分析，并探索更好的可解释性和泛化的途径。我们的代码在\url{https://github.com/bonaventuredossou/nightingale_winning_solution}上是开源的
Breast Cancer Image Classification Method Based on Deep Transfer Learning
The Hong Kong University of Science and Technology（香港科技大学）；University of Southampton（南安普顿大学）；Trine University（特莱恩大学）；Southwest Jiaotong University（西南交通大学）
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer
Stony Brook University（石溪大学）；Stony Brook Medicine（石溪医学院）；Stony Brook University Hospital（石溪大学医院）；University of Michigan Medical School（密歇根大学医学院）；National Cancer Institute（美国国家癌症研究所）；National Institutes of Health（美国国立卫生研究院）；Emory University（埃默里大学）
Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models
KTH Royal Institute of Technology（瑞典皇家理工学院）；Science for Life Laboratory（生命科学实验室）；Karolinska Institutet（卡罗林斯卡学院）；Karolinska University Hospital（卡罗林斯卡大学医院）
A survey on deep learning approaches for breast cancer diagnosis
Faculty of Engineering and Applied Science, Ontario Tech University（安大略理工大学工程与应用科学学院）；Faculty of Business and Information Technology, Ontario Tech University（安大略理工大学商业与信息技术学院）
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
The Hong Kong University of Science and Technology（香港科技大学）；Stanford University School of Medicine（斯坦福大学医学院）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳-香港协同创新研究院）
ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification
Umm Al-Qura University（乌姆·库拉大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；University of Tabuk（塔布克大学）；King Faisal Specialist Hospital and Research Center（费萨尔国王专科医院和研究中心）；University of Nevada（内华达大学）
Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning
Lovely Professional University（洛弗利专业大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Maharaja Agrasen Institute of Technology（马哈拉贾·阿格拉森技术学院）；University of Economics Ho Chi Minh City（胡志明市经济大学）
Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection
University of California, Los Angeles（加州大学洛杉矶分校）；Washington University in St. Louis（华盛顿大学圣路易斯分校）；Johns Hopkins University（约翰斯·霍普金斯大学）；Boston University（波士顿大学）；Northeastern University（东北大学）；Tulane University（杜兰大学）
Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Meta Platforms Inc.（元平台公司）
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）
FECT: Classification of Breast Cancer Pathological Images Based on Fusion Features
Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School（清华深圳国际研究生院生物制药与健康工程研究所）；School of Medicine, Nankai University（南开大学医学院）
Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis
The Chinese University of Hong Kong（香港中文大学）；Imsight Medical Technology, Co., Ltd.（推想医疗科技股份有限公司）；Imperial College London（伦敦帝国理工学院）；The Fifth Medical Center of Chinese PLA General Hospital（中国人民解放军总医院第五医学中心）；Beijing Image Diagnostic Center of Rimag（北京一脉阳光影像诊断中心）
