Standardisation of Convex Ultrasound Data Through Geometric Analysis and Augmentation
The Hamlyn Centre, Imperial College London（哈姆林中心，伦敦帝国学院）；The BioRobotics Institute, Scuola Superiore Sant’Anna（圣安娜高等学校生物机器人研究所）；Department of Imaging, Charing Cross Hospital（查令十字医院影像科）；Department of Neurosurgery, Charing Cross Hospital（查令十字医院神经外科）
Alistair Weld, Giovanni Faoro, Luke Dixon, Sophie Camp, Arianna Menciassi, Stamatia Giannarou
The application of ultrasound in healthcare has seen increased diversity and importance. Unlike other medical imaging modalities, ultrasound research and development has historically lagged, particularly in the case of applications with data-driven algorithms. A significant issue with ultrasound is the extreme variability of the images, due to the number of different machines available and the possible combination of parameter settings. One outcome of this is the lack of standardised and benchmarking ultrasound datasets. The method proposed in this article is an approach to alleviating this issue of disorganisation. For this purpose, the issue of ultrasound data sparsity is examined and a novel perspective, approach, and solution is proposed; involving the extraction of the underlying ultrasound plane within the image and representing it using annulus sector geometry. An application of this methodology is proposed, which is the extraction of scan lines and the linearisation of convex planes. Validation of the robustness of the proposed method is performed on both private and public data. The impact of deformation and the invertibility of augmentation using the estimated annulus sector parameters is also studied. Keywords: Ultrasound, Annulus Sector, Augmentation, Linearisation.
COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learning
University of Waterloo（滑铁卢大学）；McGill University（麦吉尔大学）；St. Mary’s Hospital（圣玛丽医院）；Waterloo AI Institute（滑铁卢人工智能研究院）；DarwinAI Corp.（达尔文人工智能公司）
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）
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）
ZACH-ViT: A Zero-Token Vision Transformer with ShuffleStrides Data Augmentation for Robust Lung Ultrasound Classification
Amsterdam UMC, University of Amsterdam, The Netherlands（阿姆斯特丹大学医学中心，阿姆斯特丹大学，荷兰）；Data Science Center, University of Amsterdam, The Netherlands（数据科学中心，阿姆斯特丹大学，荷兰）；Department of Intensive Care Medicine, Amsterdam UMC, University of Amsterdam & Vrije Universiteit Amsterdam, The Netherlands（重症医学科，阿姆斯特丹大学医学中心，阿姆斯特丹大学及自由大学，荷兰）
MEDDAP: Medical Dataset Enhancement via Diversified Augmentation Pipeline
School of Biomedical Engineering, University of British Columbia（不列颠哥伦比亚大学生物医学工程学院）；BC Cancer Research Institute（不列颠哥伦比亚癌症研究所）；Department of Radiology, University of British Columbia（不列颠哥伦比亚大学放射学系）；Department of Physics, University of British Columbia（不列颠哥伦比亚大学物理系）；Department of Medicine, University of British Columbia（不列颠哥伦比亚大学医学系）
Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
Research Ireland Centre for Research Training in Machine Learning（爱尔兰研究机器学习研究中心）；School of Medicine, University College Dublin（都柏林大学医学院）；School of Computer Science, University College Dublin（都柏林大学计算机科学学院）
DiffKD-DCIS: Predicting Upgrade of Ductal Carcinoma In Situ with Diffusion Augmentation and Knowledge Distillation
School of Medical Imaging, Laboratory Medicine and Rehabilitation, Xiangnan University（医学影像学院、实验室医学与康复学院，湘南大学）；Department of Radiotherapy, Affiliated Hospital (Clinical College) of Xiangnan University（放疗科，湘南大学附属医院（临床学院））；Faculty of Applied Sciences, Macao Polytechnic University（应用科学学院，澳门 polytechnic 大学）
UltraG-Ray: Physics-Based Gaussian Ray Casting for Novel Ultrasound View Synthesis
Chair for Computer Aided Medical Procedures (CAMP), Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序教席）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
PIPsUS: Self-Supervised Point Tracking in Ultrasound
The University of British Columbia（不列颠哥伦比亚大学）；Intuitive Surgical Inc.（直觉外科公司）；School of Biomedical Engineering, The University of British Columbia（不列颠哥伦比亚大学生物医学工程学院）
Stanford University School of Medicine（斯坦福大学医学院）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；University Hospital Aachen（亚琛大学医院）；Technical University of Munich（慕尼黑工业大学）；Mass General Brigham, Harvard Medical School（马萨诸塞州总医院布里厄姆妇女医院，哈佛医学院）；Dana-Farber Cancer Institute and Brigham and Women’s Hospital（达纳-法伯癌症研究所与布里格姆妇女医院）；Maastricht University（马斯特里赫特大学）；Berlin Institute of Health at Charité – Universitätsmedizin Berlin（柏林夏里特医学院柏林健康研究所）
Batch Augmentation with Unimodal Fine-tuning for Multimodal Fusion of Large Language Models
AI and Cyber Futures Institute, Charles Sturt University, Australia（人工智能与网络未来研究所，查尔斯·斯特劳特大学，澳大利亚）；Rural Health Research Institute, Charles Sturt University, Australia（农村健康研究研究所，查尔斯·斯特劳特大学，澳大利亚）；School of Computer Science and Engineering, Macau University of Science and Technology, Macao（计算机科学与工程学院，澳门科学技术大学，澳门）
针对超声图像与临床文本的多模态胎儿器官检测，提出单模态预训练、批次增强和图文特征融合方法，并在 FPU23 与 UPMC Food-101 上验证接近 SOTA 的效果。
Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks
NRP Co.（NRP公司）；Tarbiat Modares University（塔比阿特莫达勒斯大学）；AJA University of Medical Science（AJA医科大学）；Neshat Physical Medicine and Rehabilitation Center（内沙特物理医学与康复中心）
CSG: A Context-Semantic Guided Diffusion Approach in De Novo Musculoskeletal Ultrasound Image Generation
GE Healthcare（GE医疗）；Science & Technology Organization, GE Healthcare（GE医疗科技组织）；Point of Care Ultrasound & Handheld（床旁超声与 handheld（注：此处保留原文表述，结合上下文为GE医疗相关临床应用部门，按要求提取到机构级别））
UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Information and Communication, Institute of Technology of Cambodia（柬埔寨技术学院信息与通信系）；Department of Telecommunication and Network Engineering, Institute of Technology of Cambodia（柬埔寨技术学院电信与网络工程系）
Deep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound
Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University（深圳大学医学超声图像计算实验室）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人创新中心）；Affiliated Hospital of Youjiang Medical College for Nationalities（右江民族医学院附属医院）
