Ultrasound (US) imaging is one of the most commonly used non-invasive imaging techniques. However, US image acquisition requires simultaneous guidance of the transducer and interpretation of images, which is a highly challenging task that requires years of training. Despite many recent developments in intra-examination US image analysis, the results are not easy to translate to a clinical setting. We propose a generic framework to extract the US images and superimpose the results of an analysis task, without any need for physical connection or alteration to the US system. The proposed method captures the US image by tracking the screen with a camera fixed at the sonographer's view point and reformats the captured image to the right aspect ratio, in 87.66 +- 3.73ms on average.
It is hypothesized that this would enable to input such retrieved image into an image processing pipeline to extract information that can help improve the examination. This information could eventually be projected back to the sonographer's field of view in real time using, for example, an augmented reality (AR) headset.
Developmental dysplasia of the hip (DDH) is a condition in infants where the femoral head is incorrectly located in the hip joint. We propose a deep learning algorithm for segmenting key structures within ultrasound images, employing this to calculate Femoral Head Coverage (FHC) and provide a screening diagnosis for DDH. To our knowledge, this is the first study to automate FHC calculation for DDH screening. Our algorithm outperforms the international state of the art, agreeing with expert clinicians on 89.8% of our test images.
Applications of ultrasound images have expanded from fetal imaging to abdominal and cardiac diagnosis. Liver-being the largest gland in the body and responsible for metabolic activities requires to be to be diagnosed and therefore subject to utmost injury. Although, ultrasound imaging has developed into three and four dimensions providing higher amount of information; it requires highly trained medical staff due to the image complexity and dimensions it contain. Since 2D ultrasound images are still considered to be the basis of clinical treatments,computer aided automated liver diagnosis is ve…
Image-guided radiation therapy can benefit from accurate motion tracking by ultrasound imaging, in order to minimize treatment margins and radiate moving anatomical targets, e.g., due to breathing. One way to formulate this tracking problem is the automatic localization of given tracked anatomical landmarks throughout a temporal ultrasound sequence. For this, we herein propose a fully-convolutional Siamese network that learns the similarity between pairs of image regions containing the same landmark. Accordingly, it learns to localize and thus track arbitrary image features, not only predefine…
Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳市大数据研究院）
Accurate analysis of the fibrosis stage plays very important roles in follow-up of patients with chronic hepatitis B infection. In this paper, a deep learning framework is presented for automatically liver fibrosis prediction. On contrary of previous works, our approach can take use of the information provided by multiple ultrasound images. An indicator-guided learning mechanism is further proposed to ease the training of the proposed model. This follows the workflow of clinical diagnosis and make the prediction procedure interpretable. To support the training, a dataset is well-collected whic…
PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Vanderbilt University（范德堡大学）；PingAn Technology（平安科技）
Ultrasound (US) is a critical modality for diagnosing liver fibrosis. Unfortunately, assessment is very subjective, motivating automated approaches. We introduce a principled deep convolutional neural network (CNN) workflow that incorporates several innovations. First, to avoid overfitting on non-relevant image features, we force the network to focus on a clinical region of interest (ROI), encompassing the liver parenchyma and upper border. Second, we introduce global heteroimage fusion (GHIF), which allows the CNN to fuse features from any arbitrary number of images in a study, increasing its…
Graz University of Technology（格拉茨技术大学）；BioTechMed（生物技术医学）；Shanghai Jiao Tong University（上海交通大学）；Katharinenhospital（卡塔琳娜医院）；Ulm University（乌尔姆大学）
Ultrasound (US) is the most commonly used liver imaging modality worldwide. Due to its low cost, it is increasingly used in the follow-up of cancer patients with metastases localized in the liver. In this contribution, we present the results of an interactive segmentation approach for liver metastases in US acquisitions. A (semi-) automatic segmentation is still very challenging because of the low image quality and the low contrast between the metastasis and the surrounding liver tissue. Thus, the state of the art in clinical practice is still manual measurement and outlining of the metastases…
In this paper we present a new method for motion tracking of tumors in liver ultrasound image sequences. Our algorithm has two main steps. In the first step, we apply mean shift algorithm with multiple features to estimate the center of the target in each frame. Target in the first frame is defined using an ellipse. Edge, texture, and intensity features are extracted from the first frame, and then mean shift algorithm is applied to each feature separately to find the center of ellipse related to that feature in the next frame. The center of ellipse will be the weighted average of these centers…
Institute of Fundamental Technological Research, Polish Academy of Sciences（波兰科学院基础技术研究所）；Medical University of Warsaw（华沙医科大学）
Convolutional neural networks (CNNs) have achieved remarkable success in medical image analysis tasks. In ultrasound (US) imaging, CNNs have been applied to object classification, image reconstruction and tissue characterization. However, CNNs can be vulnerable to adversarial attacks, even small perturbations applied to input data may significantly affect model performance and result in wrong output. In this work, we devise a novel adversarial attack, specific to ultrasound (US) imaging. US images are reconstructed based on radio-frequency signals. Since the appearance of US images depends on …
Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong（香港中文大学深圳大数据研究院）；Huazhong University of Science and Technology（华中科技大学）；Shenzhen Third People’s Hospital（深圳市第三人民医院）
With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD). Due to the noisy data, expensive annotations of US images, the application of Artificial Intelligence (AI) assisting approaches encounters a bottleneck. Besides, the use of mono-modal US data limits the further improve of the classification results. In this work, we innovatively propose a multi-modal fusion network with active learning (MMFN-AL) for ALFD to exploit the information of multiple modalities, eliminate the noisy data and red…
Pierre and Marie Curie University（皮埃尔和玛丽居里大学）；ESPCI Paris, PSL Research University（巴黎高等物理化工学院，巴黎文理研究大学）；National University of Defense Technology（国防科技大学）；Peking University（北京大学）；Sun Yat-Sen University（中山大学）
During the last decades, the number of new full-reference image quality assessment algorithms has been increasing drastically. Yet, despite of the remarkable progress that has been made, the medical ultrasound image similarity measurement remains largely unsolved due to a high level of speckle noise contamination. Potential applications of the ultrasound image similarity measurement seem evident in several aspects. To name a few, ultrasound imaging quality assessment, abnormal function region detection, etc. In this paper, a comparative study was made on full-reference image quality assessment…
Ulm University（乌尔姆大学）；Katharinenhospital（卡塔琳娜医院）；Graz University of Technology（格拉茨技术大学）；BioTechMed（生物技术医学）
Manual segmentation of hepatic metastases in ultrasound images acquired from patients suffering from pancreatic cancer is common practice. Semiautomatic measurements promising assistance in this process are often assessed using a small number of lesions performed by examiners who already know the algorithm. In this work, we present the application of an algorithm for the segmentation of liver metastases due to pancreatic cancer using a set of 105 different images of metastases. The algorithm and the two examiners had never assessed the images before. The examiners first performed a manual segm…
Optimization-Based Calibration for Intravascular Ultrasound Volume Reconstruction
Inria；University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；INSERM（法国国家医学研究院）；ICube（ICube研究中心）；UMR7357（UMR7357研究单位）；University of Basel（巴塞尔大学）；Department of Biomedical Engineering（生物医学工程系）；Institute of Image-Guided Surgery（影像引导手术研究所）；IHU Strasbourg（斯特拉斯堡IHU）
PAII Inc.（PAII 公司）；Chang Gung Memorial Hospital, Linkou Medical Center（长庚纪念医院林口医学中心）；Institute of Molecular and Genomic Medicine, National Health Research Institutes（国家卫生研究院分子与基因组医学研究所）；Ping An Insurance Group（平安保险集团）
Background & Aims: Hepatic steatosis is a major cause of chronic liver disease. 2D ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated diagnoses are highly subjective. We developed a scalable deep learning (DL) algorithm for quantitative scoring of liver steatosis from 2D ultrasound images. Approach & Results: Using retrospectively collected multi-view ultrasound data from 3,310 patients, 19,513 studies, and 228,075 images, we trained a DL algorithm to diagnose steatosis stages (healthy, mild, moderate, or severe) from ultrasound diagnoses. Perform…
Autonomous Robotic Ultrasound System for Liver Follow-up Diagnosis: Pilot Phantom Study
School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；Center for Advanced Medical Computing and Analysis (CAMCA), Massachusetts General Hospital（麻省总医院高级医学计算与分析中心）；Harvard Medical School（哈佛医学院）
TU Graz, Institute for Computer Graphics and Vision（格拉茨技术大学计算机图形与视觉研究所）；BioTechMed（生物技术医学研究联盟）；Shanghai Jiao Tong University, School of Mechanical Engineering（上海交通大学机械工程学院）；Katharinenhospital（卡塔琳娜医院）
Ultrasound (US) is the most commonly used liver imaging modality worldwide. It plays an important role in follow-up of cancer patients with liver metastases. We present an interactive segmentation approach for liver tumors in US acquisitions. Due to the low image quality and the low contrast between the tumors and the surrounding tissue in US images, the segmentation is very challenging. Thus, the clinical practice still relies on manual measurement and outlining of the tumors in the US images. We target this problem by applying an interactive segmentation algorithm to the US data, allowing th…
FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound
Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University（广西机器视觉与智能控制重点实验室，梧州大学）；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 Ultrasound, Shenzhen Maternal and Child Healthcare Hospital（深圳妇幼保健医院超声科）；Department of Ultrasound, Sichuan Provincial Maternity and Child Healthcare Hospital（四川省妇幼保健院超声科）；Faculty of Humanities and Arts, Macau University of Science and Technology（澳门科技大学人文艺术学院）
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（Rechts der Isar 医院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Ultrasound imaging is caught between the quest for the highest image quality, and the necessity for clinical usability. Our contribution is two-fold: First, we propose a novel fully convolutional neural network for ultrasound reconstruction. Second, a custom loss function tailored to the modality is employed for end-to-end training of the network. We demonstrate that training a network to map time-delayed raw data to a minimum variance ground truth offers performance increases in a clinical environment. In doing so, a path is explored towards improved clinically viable ultrasound reconstructio…
The size and geometry of the prostate are known to be pivotal quantities used by clinicians to assess the condition of the gland during prostate cancer screening. As an alternative to palpation, an increasing number of methods for estimation of the above-mentioned quantities are based on using imagery data of prostate. The necessity to process large volumes of such data creates a need for automatic segmentation tools which would allow the estimation to be carried out with maximum accuracy and efficiency. In particular, the use of transrectal ultrasound (TRUS) imaging in prostate cancer screeni…
Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；City, University of London（伦敦城市大学）；Technical University of Munich（慕尼黑工业大学）
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill required to diagnose such malformations from live ultrasound during examination is high and resources for screening are often limited. We present an interpretable, atlas-learning segmentation method for automatic diagnosis of Hypo-plastic Left Heart Syndrome (HLHS) from a single `4 Chamber Heart' view image. We propose to extend the recently introduced Image-and-Spatial Transformer Networks (Atlas-ISTN) into a framework t…
Video is an essential imaging modality for diagnostics, e.g. in ultrasound imaging, for endoscopy, or movement assessment. However, video hasn't received a lot of attention in the medical image analysis community. In the clinical practice, it is challenging to utilise raw diagnostic video data efficiently as video data takes a long time to process, annotate or audit. In this paper we introduce a novel, fully automatic video summarization method that is tailored to the needs of medical video data. Our approach is framed as reinforcement learning problem and produces agents focusing on the prese…
Technical University of Denmark（丹麦技术大学）；CAMES Rigshospitalet（哥本哈根大学医院CAMES中心）
Confounding information in the form of text or markings embedded in medical images can severely affect the training of diagnostic deep learning algorithms. However, data collected for clinical purposes often have such markings embedded in them. In dermatology, known examples include drawings or rulers that are overrepresented in images of malignant lesions. In this paper, we encounter text and calipers placed on the images found in national databases containing fetal screening ultrasound scans, which correlate with standard planes to be predicted. In order to utilize the vast amounts of data a…
The Ohio State University（俄亥俄州立大学）；The University of Arizona（亚利桑那大学）
The rapid and seemingly endless expansion of COVID-19 can be traced back to the inefficiency and shortage of testing kits that offer accurate results in a timely manner. An emerging popular technique, which adopts improvements made in mobile ultrasound technology, allows for healthcare professionals to conduct rapid screenings on a large scale. We present an image-based solution that aims at automating the testing process which allows for rapid mass testing to be conducted with or without a trained medical professional that can be applied to rural environments and third world countries. Our co…
Tsinghua University（清华大学）；University of South Carolina（南卡罗来纳大学）；Shenzhen Hospital of Peking University（北京大学深圳医院）
Objective: Breast cancer screening is of great significance in contemporary women's health prevention. The existing machines embedded in the AI system do not reach the accuracy that clinicians hope. How to make intelligent systems more reliable is a common problem. Methods: 1) Ultrasound image super-resolution: the SRGAN super-resolution network reduces the unclearness of ultrasound images caused by the device itself and improves the accuracy and generalization of the detection model. 2) In response to the needs of medical images, we have improved the YOLOv4 and the CenterNet models. 3) Multi-…
EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence
School of iOPEN, Northwestern Polytechnical University（iOPEN学院，西北工业大学）；The First Affiliated Hospital of Sun Yat-Sen University（中山大学附属第一医院）；College of Mechanical Engineering, Tongji University（同济大学机械工程学院）
COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics
National Research Council Canada（加拿大国家研究委员会）；University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）；Oakville Trafalgar Memorial Hospital（奥克维尔特拉法加纪念医院）；McMaster University（麦克马斯特大学）
Department of Medical Physics and Biomedical Engineering（医学物理与生物医学工程系）；Wellcome Center for Human Neuroimaging（惠康人类神经成像中心）；Centre for Medical Image Computing（医学影像计算中心）；Dementia Research Centre（痴呆症研究中心）；UCL Institute of Neurology（伦敦大学学院神经学研究所）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences（惠康/工程与物理科学研究委员会介入与外科科学中心）；Department of Physics and Astronomy（物理与天文学系）；Department of Computer Science（计算机科学系）；University College London（伦敦大学学院）
When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in ground-truth labels. In this study, we evaluate the utility of a pre-screening classification network prior to the segmentation network. Experimental results demonstrate that such a classifier, minimising frame classification errors, was able to directly impact the number of false positive and false negative frames. Importantly, the segmentation accura…
University of Jordan（约旦大学）；King Abdullah II School for IT（阿卜杜拉二世信息技术学院）
Effective ultrasound tissue characterization is usually hindered by complex tissue structures. The interlacing of speckle patterns complicates the correct estimation of backscatter distribution parameters. Nakagami parametric imaging based on localized shape parameter mapping can model different backscattering conditions. However, performance of the constructed Nakagami image depends on the sensitivity of the estimation method to the backscattered statistics and scale of analysis. Using a fixed focal region of interest in estimating the Nakagami parametric image would increase estimation varia…
University of Waterloo（滑铁卢大学）；National Research Council Canada（加拿大国家研究委员会）；Oakville Trafalgar Memorial Hospital（奥克维尔特拉法加纪念医院）；McMaster University（麦克马斯特大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）
The Coronavirus Disease 2019 (COVID-19) pandemic has impacted many aspects of life globally, and a critical factor in mitigating its effects is screening individuals for infections, thereby allowing for both proper treatment for those individuals as well as action to be taken to prevent further spread of the virus. Point-of-care ultrasound (POCUS) imaging has been proposed as a screening tool as it is a much cheaper and easier to apply imaging modality than others that are traditionally used for pulmonary examinations, namely chest x-ray and computed tomography. Given the scarcity of expert ra…
Institute for Robotics and Cognitive Systems, Universität zu Lübeck（吕贝克大学机器人与认知系统研究所）
3D ultrasound (3DUS) becomes more interesting for target tracking in radiation therapy due to its capability to provide volumetric images in real-time without using ionizing radiation. It is potentially usable for tracking without using fiducials. For this, a method for learning meaningful representations would be useful to recognize anatomical structures in different time frames in representation space (r-space). In this study, 3DUS patches are reduced into a 128-dimensional r-space using conventional autoencoder, variational autoencoder and sliced-wasserstein autoencoder. In the r-space, the…
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）
As the global population continues to face significant negative impact by the on-going COVID-19 pandemic, there has been an increasing usage of point-of-care ultrasound (POCUS) imaging as a low-cost and effective imaging modality of choice in the COVID-19 clinical workflow. A major barrier with widespread adoption of POCUS in the COVID-19 clinical workflow is the scarcity of expert clinicians that can interpret POCUS examinations, leading to considerable interest in deep learning-driven clinical decision support systems to tackle this challenge. A major challenge to building deep neural networ…
FreqDINO++: A Frequency-Guided Multi-Task Routing Vision Foundation Model for Universal Ultrasound Analysis
University of Nottingham Ningbo China（宁波诺丁汉大学）；University of Nottingham（诺丁汉大学）；University of Lincoln（林肯大学）；Jinan University（暨南大学）；The Hong Kong Polytechnic University（香港理工大学）
A benchmark for 2D foetal brain ultrasound analysis
Brain and Mind Centre, University of Sydney（悉尼大学脑与思维中心）；Faculty Of Science, Yamagata University（山形大学理学院）；Hospital Universitari de Girona Doctor Josep Trueta（赫罗纳大学医院何塞普·特鲁埃塔医生医院）
Ultrasound images are one of the most widely used techniques in clinical settings to analyze and detect different organs for study or diagnoses of diseases. The dependence on subjective opinions of experts such as radiologists calls for an automatic recognition and detection system that can provide an objective analysis. Previous work done on this topic is limited and can be classified by the organ of interest. Hybrid neural networks, linear and logistic regression models, 3D reconstructed models, and various machine learning techniques have been used to solve complex problems such as detectio…
Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as we…
Shenzhen University（深圳大学）；University of Leeds（利兹大学）；KU Leuven（鲁汶大学）；Luohu People’s Hospital（罗湖人民医院）
Standard plane recognition plays an important role in prenatal ultrasound (US) screening. Automatically recognizing the standard plane along with the corresponding anatomical structures in US image can not only facilitate US image interpretation but also improve diagnostic efficiency. In this study, we build a novel multi-label learning (MLL) scheme to identify multiple standard planes and corresponding anatomical structures of fetus simultaneously. Our contribution is three-fold. First, we represent the class correlation by word embeddings to capture the fine-grained semantic and latent stati…
Explainable fetal ultrasound quality assessment with progressive concept bottleneck models
Technical University of Denmark（丹麦技术大学）；Slagelse Hospital（斯莱格瑟医院）；CAMES；Copenhagen University Hospital Rigshospitalet（哥本哈根大学医院丽格医院）；University of Copenhagen（哥本哈根大学）
Beihang University（北京航空航天大学）；XuanWu Hospital, Capital Medical University（首都医科大学宣武医院）；Beijing Diagnostic Center of Vascular Ultrasound（北京血管超声诊断中心）
Carotid arteries vulnerable plaques are a crucial factor in the screening of atherosclerosis by ultrasound technique. However, the plaques are contaminated by various noises such as artifact, speckle noise, and manual segmentation may be time-consuming. This paper proposes an automatic convolutional neural network (CNN) method for plaque segmentation in carotid ultrasound images using a small dataset. First, a parallel network with three independent scale decoders is utilized as our base segmentation network, pyramid dilation convolutions are used to enlarge receptive fields in the three segme…
Università degli Studi di Genova（热那亚大学）；IRCCS San Martino Hospital（IRCCS圣马蒂诺医院）
Due to the need for quick and effective treatments for liver diseases, which are among the most common health problems in the world, staging fibrosis through non-invasive and economic methods has become of great importance. Taking inspiration from diagnostic laparoscopy, used in the past for hepatic diseases, in this paper ultrasound images of the liver are studied, focusing on a specific region of the organ where the Glisson's capsule is visible. In ultrasound images, the Glisson's capsule appears in the shape of a line which can be extracted via classical methods in literature. By making use…
Image representations are commonly learned from class labels, which are a simplistic approximation of human image understanding. In this paper we demonstrate that transferable representations of images can be learned without manual annotations by modeling human visual attention. The basis of our analyses is a unique gaze tracking dataset of sonographers performing routine clinical fetal anomaly screenings. Models of sonographer visual attention are learned by training a convolutional neural network (CNN) to predict gaze on ultrasound video frames through visual saliency prediction or gaze-poin…
Pakistan Institute of Engineering and Applied Sciences（巴基斯坦工程与应用科学学院）；Multan Institute of Nuclear Medicine and Radiotherapy（木尔坦核医学与放射治疗研究所）
This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can chose to carry out fur…
How well do routinely collected demographic and clinical variables aid point-of-care lung ultrasound TB classification
University of Stellenbosch（斯泰伦博斯大学）；Swiss Federal Institute of Technology (EPFL)（瑞士联邦理工学院（EPFL））；National Teaching Hospital for Tuberculosis and Pulmonary Diseases (CNHU-PPC)（国家结核病与肺部疾病教学医院（CNHU-PPC））；South African Medical Research Council（南非医学研究理事会）；Stellenbosch University（斯泰伦博斯大学）
Pyramid Self-Contrastive Learning for Single-shot Test-time Ultrasound Image Denoising
Department of Electrical and Computer Engineering, The University of Hong Kong（香港大学电子与计算机工程系）；Department of Biomedical Engineering, Duke University（达特茅斯大学生物医学工程系）
Imperial College London（帝国理工学院）；ETH Zurich（苏黎世联邦理工学院）；Guy’s and St Thomas’ NHS Foundation（盖伊和圣托马斯国民保健服务基金会）
Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses. However, the accurate measurement of relevant anatomical structures is subject to large inter-observer variability in the clinic. To address this issue, an automated method utilizing Fully Convolutional Networks (FCN) is proposed to determine measurements of fetal head circumference (HC) and biparietal diameter (BPD). An FCN was trained on approximately 2000 2D ultrasound images of the head with annotations provided by 45 different sonographers during routine …
PBE-UNet: A light weight Progressive Boundary-Enhanced U-Net with Scale-Aware Aggregation for Ultrasound Image Segmentation
Department of Computer Science, Shaoxing University（绍兴大学计算机科学系）；National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University（工业智能化与系统优化国家级前沿科学中心，东北大学）；College of Computer Science, Chongqing University（重庆大学计算机学院）；School of Computer and Computing Science, Hangzhou City University（杭州市大学计算机与计算科学学院）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC研究院）；Information Technology R&D Innovation Center of Peking University（北京大学信息技术研发创新中心）
A Foundational Generative Model for Breast Ultrasound Image Analysis
Peking University（北京大学）；Peking University Cancer Hospital & Institute（北京大学肿瘤医院暨研究所）；Peking Union Medical College Hospital（北京协和医院）；Cancer Hospital, Chinese Academy of Medical Sciences（中国医学科学院肿瘤医院）；Stanford University（斯坦福大学）；Yizhun Medical AI Co., Ltd.（医准智能医疗科技有限公司）
