Lung cancer is the leading reason behind cancer-related deaths within the world. Early detection of lung nodules is vital for increasing the survival rate of cancer patients. Traditionally, physicians should manually identify the world suspected of getting carcinoma. When developing these detection systems, the arbitrariness of lung nodules' shape, size, and texture could be a challenge. Many studies showed the applied of computer vision algorithms to accurate diagnosis and classification of lung nodules. A deep learning algorithm called the VGG16 was developed during this paper to help medical professionals diagnose and classify carcinoma nodules. VGG16 can classify medical images of carcinoma in malignant, benign, and healthy patients. This paper showed that nodule detection using this single neural network had 92.08% sensitivity, 91% accuracy, and an AUC of 93%.
Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach
Kamnoetvidya Science Academy（甘烹碧科学学院）；National Electronics and Computer Technology Center（泰国国家电子和计算机技术中心）；Vidyasirimedhi Institute of Science and Technology（瓦莱拉克科学技术研究院）
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
College of Aerospace Science and Engineering, National University of Defense Technology（国防科技大学航空航天科学与工程学院）；Eindhoven University of Technology（埃因霍温理工大学）；ScreenPoint Medical（ScreenPoint Medical 公司）；Pingan Technology（平安科技）；Lunit Inc.（Lunit 公司）；Queen Mary University of London（伦敦玛丽女王大学）；Beihang University（北京航空航天大学）；Beijing Advanced Innovation Center for Biomedical Engineering（北京生物医学工程高精尖创新中心）
Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images
University of Technology Sydney（悉尼科技大学）；IBM Australia Limited（IBM澳大利亚有限公司）；Sejong University（世宗大学）；Universiti Kebangsaan Malaysia（马来西亚国民大学）；Charles Sturt University（查尔斯特大学）
Lung cancer screening with low-dose CT scans using a deep learning approach
Arkansas State University（阿肯色州立大学）；University of Michigan（密歇根大学）；Ann Arbor Algorithm（安阿伯算法公司）；University of Arkansas at Pine Bluff（阿肯色大学松崖分校）；University of Arkansas for Medical Sciences（阿肯色大学医学院）
Medical Artificial Intelligence for Early Detection of Lung Cancer: A Survey
College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, 610225, China（计算机科学与人工智能学院，西南民族大学）；The Australian National University, Canberra ACT 2601, Australia（澳大利亚国立大学）；School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China（计算机科学与技术学校，北京交通大学）；Hunan university of technology, Zhuzhou, 410072, China（湖南理工大学）；University of Adelaide, Adelaide, South Australia 5005, Australia（阿德莱德大学）；La Trobe University, Melbourne, Victoria 3086, Australia（拉筹伯大学）
Comparative Analysis of Machine Learning Models for Lung Cancer Mutation Detection and Staging Using 3D CT Scans
Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University（斯坦福大学生物医学信息学研究中心（BMIR），医学系）；National Center for AI (NCAI), Saudi Data and AI Authority (SDAIA)（沙特数据与人工智能管理局国家人工智能中心（NCAI））；Department of Biomedical Data Science, Stanford University（斯坦福大学生物医学数据科学系）
A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans
Draper（德雷珀实验室）；Google（谷歌）
我们介绍了一种新的计算机辅助检测和诊断系统，肺癌筛查与低剂量CT扫描，产生有意义的概率评估。我们的系统完全基于3D卷积神经网络，并在公开的LUNA16和Kaggle Data Science Bowl挑战赛上实现了肺结节检测和恶性肿瘤分类任务的最新性能。虽然结节检测系统通常是自行设计和优化的，但我们发现，考虑检测和诊断组件之间的耦合是很重要的。利用这种耦合使我们能够开发一种端到端系统，该系统具有更高和更强大的性能，并消除了对结节检测假阳性减少阶段的需要。此外，我们在我们的深度学习系统中描述了模型的不确定性，这是肺部CT分析的第一个，并表明我们可以使用它来为结节检测和患者恶性肿瘤诊断提供校准良好的分类概率。这些由模型不确定性提供信息的校准概率可用于后续基于风险的诊断干预或疾病治疗决策，正如我们使用基于概率的患者转诊策略进一步改善我们的结果所证明的那样。
AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons
BAMF Health（BAMF健康公司）；National Institute of Health（美国国立卫生研究院）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究实验室）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
Fairness Evaluation of Risk Estimation Models for Lung Cancer Screening
Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center（放射医学成像分析组，医学影像系，拉德堡德大学医学中心）；Department of Artificial Intelligence, Radboud University（人工智能系，拉德堡德大学）；Ethics of Healthcare Group, Department IQ Health, Radboud University Medical Center（医疗伦理组，IQ健康系，拉德堡德大学医学中心）
A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation
School of Computer Science and Technology, Shandong University of Finance and Economics（山东财经大学计算机科学与技术学院）；School of Information Science and Technology, Linyi University（临沂大学信息科学与技术学院）；Thoracic Surgery Department of Linyi Central Hospital（临沂市中心医院胸外科）；Personnel Department of Linyi Central Hospital（临沂市中心医院人事科）
Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection
Laboratoire LITAN École supérieure en Sciences et Technologies de l’Informatique et du Numérique（高等计算机与数字科学技术学院 LITAN 实验室）；College of Engineering and Information Technology, University of Dubai（迪拜大学工程与信息技术学院）；Department of Computer Science, University of Sharjah（沙迦大学计算机科学系）；Institute of Artificial Intelligence, De Montfort University（德蒙福特大学人工智能研究所）
Classification based deep learning models for lung cancer and disease using medical images
Laboratory for Artificial Intelligence of Personalized Medicine, School of Artificial Intelligence, Guilin University of Electronic Technology（人工智能个性化医学实验室，人工智能学院，桂林电子科技大学）；The Laboratory for Imagery, Vision and Artificial Intelligence, Ecole de Technologie Superieure（影像、视觉与人工智能实验室，蒙特利尔理工学院）
Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer
Hunan University（湖南大学）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；Central South University（中南大学）；Hunan Clinical Medical Research Center for Cancer Pathogenic Genes Testing and Diagnosis（湖南省肿瘤致病基因检测与诊断临床医学研究中心）；Shanghai Cancer Institute（上海市肿瘤研究所）；Renji Hospital, School of Medicine, Shanghai Jiaotong University（上海交通大学医学院附属仁济医院）；Shanghai Jiaotong University（上海交通大学）
Dual-Modal Lung Cancer AI: Interpretable Radiology and Microscopy with Clinical Risk Integration
Hatyaiwittayalai School（哈雅维泰莱学校）；School of Information, Computer, and Communication Technology（信息、计算机与通信技术学院）；Sirindhorn International Institute of Technology（Sirindhorn国际技术学院）；Thammasat University（泰国朱拉大学）；Pathum Thani, Thailand（帕亨他尼，泰国）
A Deep Convolutional Neural Network for Lung Cancer Diagnostic
Universitat Rovira i Virgili（罗维拉-威尔吉利大学）；University of Cagliari（卡利亚里大学）
在本文中，我们研究了深度学习技术在医学图像分析问题上诊断肺癌的优势。卷积神经网络（CNN）模型由于其在生成高层次图像表示方面的良好效果而成为模式识别和计算机视觉研究领域的热门。我们提出了一种新的深度学习架构，用于学习高级图像表示，以在医学图像二值分类任务中实现低方差的高分类精度。我们的目标是在深度卷积神经网络的开始学习判别紧凑特征。我们在Kaggle Data Science Bowl 2017（KDSB17）数据集上评估了我们的模型，并将其与Kaggle竞赛中提出的一些相关作品进行了比较。
MSAD-Net: Multiscale and Spatial Attention-based Dense Network for Lung Cancer Classification
Computer Science and Engineering Department, NIIT University, Jaipur, India（尼特大学计算机科学与工程系，印度斋普尔）；CSE department, Christ (Deemed to be University), Bangalore, India（基督大学（认定为大学）计算机科学与工程系，印度班加罗尔）；Faculty of Computing, Engineering and the Built Environment, Birmingham City University, UK（伯明翰城市大学计算、工程与建筑环境学院，英国）
SMILE: a Scale-aware Multiple Instance Learning Method for Multicenter STAS Lung Cancer Histopathology Diagnosis
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与通信工程学院）；College of Information Science and Engineering, Hunan Normal University（湖南师范大学信息科学与工程学院）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）
Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach
NMAM Institute of Technology（NMAM理工学院）；Visvesvaraya Technological University（韦斯韦斯瓦拉亚理工大学）；Sahyadri College of Engineering and Management（萨亚德里工程与管理学院）；JSS Academy of Technical Education（JSS技术教育学院）
Evaluating LeNet Algorithms in Classification Lung Cancer from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases
Faculty of Electrical and Computer Engineering, Islamic Azad University（伊斯兰阿扎德大学电气与计算机工程学院）；Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University（伊斯兰阿扎德大学阿尔达比尔分校青年研究者与精英俱乐部）；Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases（伊拉克肿瘤学教学医院/国家癌症疾病中心）
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning
University College London（伦敦大学学院）；MANAS AI（MANAS人工智能公司）；University College London Hospitals NHS Foundation Trust（伦敦大学学院医院国民保健服务基金会信托）；Royal Brompton and Harefield NHS Foundation Trust（皇家布朗普顿与哈菲尔德国民保健服务基金会信托）；Imperial College（帝国理工学院）
Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures
National Yang Ming Chiao Tung University（阳明交通大学）；National Taiwan University Cancer Center（台湾大学医学院附设癌医中心医院）；National Taiwan University Hospital（台湾大学医学院附设医院）；National Taiwan University Hsin-Chu Hospital（台湾大学医学院附设新竹医院）
