In this article we suggest a fast multi-scale edge-detection scheme for medical ultrasound signals. The edge-detector is based on well-known properties of the continuous wavelet trans- form. To achieve both good localization of edges and detect only significant edges, we study the maxima-lines of the wavelet transform. One can obtain the maxima-lines between two scales by computing the wavelet transform at several intermediate scales. To reduce computational effort and time we suggest a time-scale filtering procedure which uses only few scales to connect modulus-maxima across time-scale plane. The design of this procedure is based on a study of maxima-lines corresponding to edges typical for medical ultrasound signals. This study allows us to construct an algorithm for medical ultrasound signals which meets the demand for speed, but not on expense of reliability. The edge-detection algorithm has been applied to a large class of medical ultrasound sig- nals including tumour-, liver- and artery-images. Our results show that the proposed algorithm effectively detects major features in such signals, including edges with low contrast.
BAAF: A benchmark attention adaptive framework for medical ultrasound image segmentation tasks
College of Biomedical Engineering and Technology, Tianjin Medical University（天津医科大学生物医学工程学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；Department of Urology, Fourth Medical Center of PLA General Hospital（中国人民解放军第四医学中心泌尿外科科）；Department of Urology, Civil Aviation General Hospital（民航总医院泌尿外科科）；College of Artificial Intelligence, Nankai University（南开大学人工智能学院）；Department of Ultrasonography, Tianjin Medical University Second Hospital（天津医科大学第二医院超声科）
A Labeled Ophthalmic Ultrasound Dataset with Medical Report Generation Based on Cross-modal Deep Learning
School of Electrical and Control Engineering, North China University of Technology（北方工业大学电气与控制工程学院）；School of Automation and Electrical Engineering, Shenyang Ligong University（沈阳理工大学自动化与电气工程学院）；The Fourth Affiliated Hospital of China Medical University（中国医科大学附属第四医院）
CP-UNet: Contour-based Probabilistic Model for Medical Ultrasound Images Segmentation
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；Tianjin Key Laboratory of Cognitive Computing and Application（天津认知计算与应用重点实验室）；Tianjin Key Laboratory of Advanced Networking（天津先进网络重点实验室）；School of Future Technology, Tianjin University（天津大学未来技术学院）
Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning
University of California, San Diego（加利福尼亚大学圣地亚哥分校）；Institute of Fundamental Technological Research, Polish Academy of Sciences（波兰科学院基础技术研究所）；Polish-Japanese Academy of Information Technology（波兰-日本信息技术学院）；The Maria Skłodowska-Curie Memorial Cancer Centre and Institute of Oncology（玛丽·斯克洛多夫斯卡-居里纪念癌症中心和肿瘤研究所）
The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound
Cheriton School of Computer Science, University of Waterloo（滑铁卢大学计算机科学学院）；Department of Systems Design Engineering, University of Waterloo（滑铁卢大学系统设计工程系）；Schulich School of Medicine and Dentistry, Western University（西部大学施乐希医学与牙科学校）
Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis
Department of Health Technology and Informatics, The Hong Kong Polytechnic University（健康科技与信息学系，香港理工大学）；Centre for Smart Health and School of Nursing, The Hong Kong Polytechnic University（智能健康中心及护理学院，香港理工大学）；Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong（影像与介入放射学系，香港中文大学）；Suzhou Hospital of Traditional Chinese Medicine Affiliated to Nanjing University of Chinese Medicine（南京中医药大学附属苏州中医医院）；Department of Ultrasound, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University（南京医科大学附属常州第二人民医院超声科）
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；The Affiliated Hospital of Qingdao University（青岛大学附属医院）；University of Idaho（爱达荷大学）
LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound
ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC学院）；University of Washington（华盛顿大学）；Zhejiang University School of Medicine Sir Run Run Shaw Hospital（浙江大学医学院附属邵逸夫医院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning
The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening
Hunan University（湖南大学）；The Hong Kong University of Science and Technology（香港科技大学）；Anhui University（安徽大学）；Shenzhen Maternity and Child Healthcare Hospital（深圳市妇幼保健院）；Sichuan Provincial Maternity and Child Health Care Hospital（四川省妇幼保健院）；National University of Singapore（新加坡国立大学）；Yunnan Maternal and Child Health Hospital（云南省妇幼保健院）；Zhejiang University（浙江大学）
EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation
Imperial College London（帝国理工学院）；University of Birmingham（伯明翰大学）；Ultromics Ltd.（奥特罗米克斯有限公司）；University of Oxford（牛津大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
Vision Models for Medical Imaging: A Hybrid Approach for PCOS Detection from Ultrasound Scans
Department of CSE, CCN University of Science & Technology（计算机科学与工程系，CCN科学与技术大学）；Department of EEE,International Islamic University Chittagong（电子工程系，国际伊斯兰大学恰tagong分校）；Faculty of Engineering, Multimedia University（工程学院，多媒体大学）；Department of CSE, Stamford University of Bangladesh（计算机科学与工程系，斯塔福德大学孟加拉国分校）；Department of Biology, Lucknow University（生物学系，拉胡尔大学）；Department of CSE, Bangladesh Army International University of Science & Technology（计算机科学与工程系，孟加拉国军队国际科学与技术大学）
U2-BENCH: Benchmarking Large Vision-Language Models on Ultrasound Understanding
Fudan University（复旦大学）；University of Oxford（牛津大学）；Dolphin AI；Beihang University（北京航空航天大学）；Hong Kong Baptist University（香港浸会大学）；University of Cambridge（剑桥大学）；The Chinese University of Hong Kong（香港中文大学）；Esonic Imaging；Technical University of Denmark（丹麦技术大学）
Medical-Knowledge Driven Multiple Instance Learning for Classifying Severe Abdominal Anomalies on Prenatal Ultrasound
Medical Ultrasound Image Computing (MUSIC) Lab（医学超声图像计算（MUSIC）实验室）；School of Biomedical Engineering（生物医学工程学院）；Medical School（医学院）；Shenzhen University（深圳大学）；Shenzhen RayShape Medical Technology Co., Ltd（深圳RayShape医疗科技有限公司）；Center for Medical Ultrasound（医学超声中心）；The Affiliated Suzhou Hospital of Nanjing Medical University（南京医科大学附属苏州医院）；Suzhou Municipal Hospital（苏州 municipal 医院）；Gusu School（古苏学校）；Nanjing Medical University（南京医科大学）；The First Hospital of China Medical University（中国医科大学第一医院）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；Nanjing Women and Children's Healthcare Hospital（南京妇女儿童保健医院）；Women's Hospital of Naniing Medical University（南京医科大学妇女医院）；Maternal and Child Health Hospital of Hubei Province（湖北省妇幼保健医院）；Northwest Women’s and Children’s Hospital（西北妇女儿童医院）；School of Artificial Intelligence（人工智能学院）；National Engineering Laboratory for Big Data System Computing Technology（大数据系统计算技术国家工程实验室）；School of Biomedical Engineering and Informatics（生物医学工程与信息学院）
Ultrasound Report Generation with Cross-Modality Feature Alignment via Unsupervised Guidance
Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Shenzhen Institute of Advanced Technology, the Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Chair for Computer Aided Medical Procedures and Augmented Reality (CAMP), Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序与增强现实教席）；The Third Medical Centre of Chinese PLA General Hospital（中国人民解放军总医院第三医学中心）；The Second School of Clinical Medicine, Southern Medical University（南方医科大学第二临床医学院）
Multiscale Switch for Semi-Supervised and Contrastive Learning in Medical Ultrasound Image Segmentation
Department of Health Technology and Informatics, The Hong Kong Polytechnic University（健康科技与信息学系，香港理工大学）；Centre for Smart Health and School of Nursing, The Hong Kong Polytechnic University（智能健康中心及护理学院，香港理工大学）；Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong（影像与介入放射科，香港中文大学）
UltraAD: Fine-Grained Ultrasound Anomaly Classification via Few-Shot CLIP Adaptation
Computer Aided Medical Procedures (CAMP)（计算机辅助医疗程序）；TU Munich, Germany（慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；The First Affiliated Hospital of Sun Yat-Sen University（中山大学附属第一医院）
POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)
ETH Zurich（苏黎世联邦理工学院）；Clinique des Grangettes（格兰奇诊所）；Biomedical Research Institute August Pi i Sunyer（奥古斯特·皮·苏涅尔生物医学研究所）；Technische Universität München（慕尼黑工业大学）
General Methods Make Great Domain-specific Foundation Models: A Case-study on Fetal Ultrasound
University of Copenhagen（哥本哈根大学）；Technical University of Denmark（丹麦技术大学）；Copenhagen Academy of Medical Education and Simulation (CAMES)（哥本哈根医学教育与模拟学院）；Copenhagen University Hospital Rigshospitalet（哥本哈根大学医院拉格比医院）；Brown University（布朗大学）；Pioneer Centre for AI（先锋人工智能中心）
Mamba-3D as Masked Autoencoders for Accurate and Data-Efficient Analysis of Medical Ultrasound Videos
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Hupan Laboratory（湖畔实验室）
