Advancements in Feature Extraction Recognition of Medical Imaging Systems Through Deep Learning Technique
Marquette University（马凯特大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；New York University（纽约大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Southern California（南加州大学）；University of Houston（休斯顿大学）
Qishi Zhan, Dan Sun, Erdi Gao, Yuhan Ma, Yaxin Liang, Haowei Yang
This study introduces a novel unsupervised medical image feature extraction method that employs spatial stratification techniques. An objective function based on weight is proposed to achieve the purpose of fast image recognition. The algorithm divides the pixels of the image into multiple subdomains and uses a quadtree to access the image. A technique for threshold optimization utilizing a simplex algorithm is presented. Aiming at the nonlinear characteristics of hyperspectral images, a generalized discriminant analysis algorithm based on kernel function is proposed. In this project, a hyperspectral remote sensing image is taken as the object, and we investigate its mathematical modeling, solution methods, and feature extraction techniques. It is found that different types of objects are independent of each other and compact in image processing. Compared with the traditional linear discrimination method, the result of image segmentation is better. This method can not only overcome the disadvantage of the traditional method which is easy to be affected by light, but also extract the features of the object quickly and accurately. It has important reference significance for clinical diagnosis.
Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects
Faculty of Informatics and Data Science, University of Regensburg（雷根斯堡大学信息学与数据科学学院）；University of California, Davis（加州大学戴维斯分校）；Motilal Nehru National Institute of Technology Allahabad（莫蒂拉尔·尼赫鲁国立阿拉哈巴德技术学院）；Faculty of Electrical Engineering and Information Technology, RWTH Aachen University（亚琛工业大学电气工程与信息技术学院）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）
A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis
Interdisciplinary Research Center for Finance and Digital Economy, King Fahd University of Petroleum and Minerals（金融与数字经济交叉研究中心，国王法赫德石油和矿物大学）；Department of Software Engineering, Faculty of Information Technology, Al-Ahliyya Amman University（软件工程系，信息科技学院，阿尔阿赫利亚大学）；Department of Information Systems and Operations Management, King Fahd University of Petroleum and Minerals（信息系统与运营管理系，国王法赫德石油和矿物大学）；ADAPT Research Centre, School of Computer Science, University of Galway（ADAPT研究中心，计算机科学学院，Galway大学）；Department of Mechanical and Nuclear Engineering, Khalifa University（机械与核工程系，哈利法大学）
Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging
Concordia University（康考迪亚大学）；Ecole de Technologie Superieure (ETS)（高等技术学院）；Laval University（拉瓦尔大学）；Khalifa University（哈利法大学）；Lebanese American University（黎巴嫩美国大学）
A Context-Aware Middleware for Medical Image Based Reports: An approach based on image feature extraction and association rules
Department of Computer Science（计算机科学系）；Departament of Mathematics & Statistics（数学与统计学系）；Universidade Federal Fluminense（联邦Fluminense大学）；York University（约克大学）
Mamba Based Feature Extraction And Adaptive Multilevel Feature Fusion For 3D Tumor Segmentation From Multi-modal Medical Image
School of Computer Science and Engineering, Central South University, Changsha, 410083, China（计算机科学与工程学院，中南大学，长沙，410083，中国）；Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University, Changsha, 410083, China（机器视觉与智能医学工程研究中心，中南大学，长沙，410083，中国）；Department of Radiation Oncology, Washington University in St. Louis, USA（放射肿瘤科，华盛顿大学圣路易斯分校，美国）；Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, France（核医学科，亨利·贝克勒尔癌症中心，鲁昂，法国）；University of Rouen-Normandy, AMIS - QuantIF UR 4108, F-76000, Rouen, France（鲁昂-诺曼底大学，AMIS - QuantIF UR 4108，法国，F-76000，鲁昂，法国）
DeformCL: Learning Deformable Centerline Representation for Vessel Extraction in 3D Medical Image
Yizhun Medical AI Co., Ltd（亿智臻医疗人工智能有限公司）；Center for Data Science, Peking University（北京大学数据科学中心）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）；State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（北京大学人工智能国家重点实验室）；Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China（琶洲实验室（黄埔），广州，广东，中国）
Recent Advances in Medical Imaging Segmentation: A Survey
Junia, UMR 8520, CNRS, Centrale Lille, Univerity of Polytechnique Hauts-de-France（Junia研究院、法国国家科学研究中心、Centrale Lille、法国高等技术大学 Hauts-de-France）；Sorbonne Center for Artificial Intelligence (SCAI), Abu Dhabi, UAE（索邦人工智能中心（SCAI））
Feature extraction with regularized siamese networks for outlier detection: application to lesion screening in medical imaging
Univ Lyon（里昂大学）；INSA‐Lyon（里昂国立应用科学学院）；Université Claude Bernard Lyon 1（里昂第一大学）；UJM-Saint Etienne（圣艾蒂安让·莫奈大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；CREATIS UMR 5220（CREATIS联合研究单位5220）；U1206（U1206研究单位）
Fusing Medical Image Features and Clinical Features with Deep Learning for Computer-Aided Diagnosis
Beijing Institute of Technology（北京理工大学）；Xuanwu Hospital, Capital Medical University（首都医科大学宣武医院）；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/国家肿瘤临床医学研究中心））
Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation
New York University（纽约大学）；Texas Tech University（得克萨斯理工大学）；University of Houston（休斯顿大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Michigan（密歇根大学）；University of Pennsylvania（宾夕法尼亚大学）
Multi-modal Medical Neurological Image Fusion using Wavelet Pooled Edge Preserving Autoencoder
Visvesvaraya National Institute of Technology Nagpur（维斯瓦瓦拉亚那格浦尔国家理工学院）；Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；All India Institute of Medical Sciences Nagpur（全印那格浦尔医学科学研究所）
Paying Per-label Attention for Multi-label Extraction from Radiology Reports
Canon Medical Research Europe（佳能医疗欧洲研究中心）；University of St Andrews（圣安德鲁斯大学）；Institute of Neuroscience & Psychology, University of Glasgow（格拉斯哥大学神经科学与心理学研究所）；University of Edinburgh（爱丁堡大学）
