Detection of pulmonary nodules in chest CT imaging plays a crucial role in early diagnosis of lung cancer. Manual examination is highly time-consuming and error prone, calling for computer-aided detection, both to improve efficiency and reduce misdiagnosis. Over the years, a range of systems have been proposed, mostly following a two-phase paradigm with: 1) candidate detection, 2) false positive reduction. Recently, deep learning has become a dominant force in algorithm development. As for candidate detection, prior art was mainly based on the two-stage Faster R-CNN framework, which starts with an initial sub-net to generate a set of class-agnostic region proposals, followed by a second sub-net to perform classification and bounding-box regression. In contrast, we abandon the conventional two-phase paradigm and two-stage framework altogether and propose to train a single network for end-to-end nodule detection instead, without transfer learning or further post-processing. Our feature learning model is a modification of the ResNet and feature pyramid network combined, powered by RReLU activation. The major challenge is the condition of extreme inter-class and intra-class sample imbalance, where the positives are overwhelmed by a large negative pool, which is mostly composed of easy and a handful of hard negatives. Direct training on all samples can seriously undermine training efficacy. We propose a patch-based sampling strategy over a set of regularly updating anchors, which narrows sampling scope to all positives and only hard negatives, effectively addressing this issue. As a result, our approach substantially outperforms prior art in terms of both accuracy and speed. Finally, the prevailing FROC evaluation over [1/8, 1/4, 1/2, 1, 2, 4, 8] false positives per scan, is far from ideal in real clinical environments. We suggest FROC over [1, 2, 4] false positives as a better metric.
Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection
University of Groningen, University Medical Center Groningen（格罗宁根大学医学中心）；Tianjin Medical University Cancer Institute and Hospital（天津医科大学肿瘤医院）；University of Twente（特温特大学）
RUN:Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection
University of Electronic Science and Technology of China（电子科技大学）；School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）
M3Net: A Macro-to-Meso-to-Micro Clinical-inspired Hierarchical 3D Network for Pulmonary Nodule Classification
Hefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥医疗健康研究院、健康与医疗技术研究所、物理研究所）；University of Science and Technology of China（中国科学技术大学）；Graduate School, Bengbu Medical College（蚌埠医疗学院研究生院）；Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC)（中国科学技术大学附属第一医院呼吸与危重症医学科、生命科学与医学学院）；Northeastern University（东北大学）；Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）；College of Computing and Data Science (CCDS), Nanyang Technological University（南洋理工大学计算与数据科学学院）
Pulmonary Nodule Malignancy Classification Using its Temporal Evolution with Two-Stream 3D Convolutional Neural Networks
Eurecat, Centre Tecnòlogic de Catalunya（加泰罗尼亚技术中心Eurecat）；Universitat Pompeu Fabra（庞培法布拉大学）；Vall d’Hebron University Hospital（瓦尔德希伯伦大学医院）；ICREA（加泰罗尼亚研究与高级研究院）
A Data Augmentation Method and the Embedding Mechanism for Detection and Classification of Pulmonary Nodules on Small Samples
Dali University（大理大学）；Liuzhou Vocational & Technical College（柳州职业技术学院）；The Second Xiangya Hospital of Central South University（中南大学湘雅二医院）；Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer（湖南省肺癌早期诊断与精准治疗重点实验室）；Early-Stage Lung Cancer Center（早期肺癌中心）
LAND: Lung and Nodule Diffusion for 3D Chest CT Synthesis with Anatomical Guidance
Eurecat, Centre Tecnològic de Catalunya（埃鲁卡特技术中心，加泰罗尼亚技术中心）；Dept. de Matemàtiques i Informàtica, Universitat de Barcelona（巴塞罗那大学数学与计算机系）；Barcelona Supercomputing Center (BSC)（巴塞罗那超级计算中心）
Application of Computer Deep Learning Model in Diagnosis of Pulmonary Nodules
University of California, Davis（加州大学戴维斯分校）；University of Washington（华盛顿大学）；Northern Arizona University（北亚利桑那大学）；Arizona State University（亚利桑那州立大学）；University of Pennsylvania（宾夕法尼亚大学）；University of California San Diego（加州大学圣迭戈分校）
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge
Radboud University Medical Center（拉德堡德大学医学中心）；Polytechnic University of Turin（都灵理工大学）；Istituto Nazionale di Fisica Nucleare（国家核物理研究院）；Radboud University Nijmegen（拉德堡德大学）；Radboud University（拉德堡德大学）；The Chinese University of Hong Kong（香港中文大学）；University of Pisa（比萨大学）
Re-Identification and Growth Detection of Pulmonary Nodules without Image Registration Using 3D Siamese Neural Networks
Eurecat Centre Tecnològic de Catalunya（加泰罗尼亚埃urecat技术中心）；Universitat Pompeu Fabra（庞培法布拉大学）；Vall d’Hebron University Hospital（瓦尔德希伯伦大学医院）；ICREA（加泰罗尼亚研究与高等研究院）
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
Massachusetts Institute of Technology（麻省理工学院）；Philips Research North America（飞利浦北美研究院）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝斯以色列女执事医疗中心）
Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images
Massachusetts Institute of Technology（麻省理工学院）；Philips Research North America（飞利浦北美研究院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）
CPLOYO: A Pulmonary Nodule Detection Model with Multi-Scale Feature Fusion and Nonlinear Feature Learning
Tongji Hospital, School of Medicine, Tongji University（同济大学医学院附属同济医院）；Shanghai Pulmonology Hospital, School of Medicine, Tongji University（同济大学医学院附属上海肺科医院）；Carnegie Mellon University（卡内基梅隆大学）
MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule
College of Computer Science and Artificial Intelligence, Southwest Minzu University（西南民族大学计算机科学与人工智能学院）；School of Intelligent System Engineering, Sun Yat-sen University（中山大学智能系统工程学院）；University of Warwick（华威大学）；Australian National University（澳大利亚国立大学）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；University of Toronto（多伦多大学）；University of Adelaide（阿德莱德大学）；La Trobe University（拉筹伯大学）
Evolving the pulmonary nodules diagnosis from classical approaches to deep learning aided decision support: three decades development course and future prospect
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；The First Affiliated Hospital of Guangzhou Medical University（广州医科大学附属第一医院）；State Key Laboratory of Respiratory Disease（呼吸疾病国家重点实验室）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Chinese Academy of Sciences（中国科学院大学）
Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction
Shanghai Jiao Tong University（上海交通大学）；Imperial College London（帝国理工学院）；Shanghai Chest Hospital（上海市胸科医院）；Shanghai Center for Brain Science and Brain-Inspired Technology（上海脑科学与类脑研究中心）
Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation
Wuhan University（武汉大学）；Artificial Intelligence Institute of Wuhan University（武汉大学人工智能研究院）；Swiss Federal Institute of Technology Lausanne (EPFL)（洛桑联邦理工学院（EPFL））；Dianei Technology（点内科技）
