HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining
Shanghai Jiaotong University（上海交通大学）；CloudWalk Technology Co., Ltd（云从科技有限公司）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Chunhui Zhang, Yixiong Chen, Li Liu, Qiong Liu, Xi Zhou
The self-supervised ultrasound (US) video model pretraining can use a small amount of labeled data to achieve one of the most promising results on US diagnosis. However, it does not take full advantage of multi-level knowledge for learning deep neural networks (DNNs), and thus is difficult to learn transferable feature representations. This work proposes a hierarchical contrastive learning (HiCo) method to improve the transferability for the US video model pretraining. HiCo introduces both peer-level semantic alignment and cross-level semantic alignment to facilitate the interaction between different semantic levels, which can effectively accelerate the convergence speed, leading to better generalization and adaptation of the learned model. Additionally, a softened objective function is implemented by smoothing the hard labels, which can alleviate the negative effect caused by local similarities of images between different classes. Experiments with HiCo on five datasets demonstrate its favorable results over state-of-the-art approaches. The source code of this work is publicly available at https://github.com/983632847/HiCo.
Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image Recognition
West China Hospital, Sichuan University（四川大学华西医院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Sichuan University（四川大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
Fudan University（复旦大学）；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（深圳市大数据研究院）；Southern University of Science and Technology（南方科技大学）
Most deep neural networks (DNNs) based ultrasound (US) medical image analysis models use pretrained backbones (e.g., ImageNet) for better model generalization. However, the domain gap between natural and medical images causes an inevitable performance bottleneck. To alleviate this problem, an US dataset named US-4 is constructed for direct pretraining on the same domain. It contains over 23,000 images from four US video sub-datasets. To learn robust features from US-4, we propose an US semi-supervised contrastive learning method, named USCL, for pretraining. In order to avoid high similarities…
SonoCLIP: Mask-Guided Region-Aware Vision-Language Pretraining for Fetal Ultrasound Analysis
School of Computer Science, Wuhan University, Wuhan, China（武汉大学计算机学院，武汉，中国）；Institute of Artificial Intelligence, Wuhan University, Wuhan, China（武汉大学人工智能研究院，武汉，中国）；Department of Ultrasound, Renmin Hospital of Wuhan University, Wuhan, China（武汉大学仁民医院超声科，武汉，中国）；National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China（武汉大学多媒体软件国家工程研究中心，武汉，中国）；Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China（湖北省多媒体与网络通信工程重点实验室，武汉大学，武汉，中国）
Wrong Organ, Right Physics: Transferring Echocardiography Pretraining to Lung Ultrasound for Tuberculosis Screening
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）
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）
Shenzhen University（深圳大学）；Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University（中山大学孙逸仙纪念医院）
Breast cancer is one of the leading causes of cancer deaths in women. As the primary output of breast screening, breast ultrasound (US) video contains exclusive dynamic information for cancer diagnosis. However, training models for video analysis is non-trivial as it requires a voluminous dataset which is also expensive to annotate. Furthermore, the diagnosis of breast lesion faces unique challenges such as inter-class similarity and intra-class variation. In this paper, we propose a pioneering approach that directly utilizes US videos in computer-aided breast cancer diagnosis. It leverages ma…
From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning
FNii-Shenzhen, The Chinese University of Hong Kong, Shenzhen（深圳FNii，香港中文大学（深圳））；School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（科学与工程学院，香港中文大学（深圳））；West China Hospital of Sichuan University, Chengdu（四川大学华西医院）；School of Computer Science, University College Dublin, Ireland（计算机科学学院，都柏林大学（爱尔兰））；College of Computer Science and Software Engineering, Shenzhen University, Shenzhen（计算机科学与软件工程学院，深圳大学）；South China Hospital, Health Science Center, Shenzhen University, Shenzhen（南方医院，深圳大学健康科学中心）；School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing（计算机科学学院，南京邮电大学）；Affiliated Hospital of North Sichuan Medical College, Sichuan（北川医学院附属医院）；North Sichuan Medical College, Sichuan（北川医学院）；Shenzhen Research Institute of Big Data, Shenzhen（深圳大数据研究院）；Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen（生物计算研究中心，哈尔滨工业大学（深圳））；School of Computer Science, Wuhan University, Wuhan（计算机科学学院，武汉大学）；Computer Science Program, Computer, E（计算机科学项目，计算机）
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）
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Contrastive learning has proven useful in many applications where access to labelled data is limited. The lack of annotated data is particularly problematic in medical image segmentation as it is difficult to have clinical experts manually annotate large volumes of data such as cardiac structures in ultrasound images of the heart. In this paper, We propose a self supervised contrastive learning method to segment the left ventricle from echocardiography when limited annotated images exist. Furthermore, we study the effect of contrastive pretraining on two well-known segmentation networks, UNet …
