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）
Michal Byra, Tomasz Sznajder, Danijel Korzinek, Hanna Piotrzkowska-Wroblewska, Katarzyna Dobruch-Sobczak, Andrzej Nowicki, Krzysztof Marasek
Deep learning algorithms, especially convolutional neural networks, have become a methodology of choice in medical image analysis. However, recent studies in computer vision show that even a small modification of input image intensities may cause a deep learning model to classify the image differently. In medical imaging, the distribution of image intensities is related to applied image reconstruction algorithm. In this paper we investigate the impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning. Due to high dynamic range raw ultrasonic signals are commonly compressed in order to reconstruct B-mode images. Based on raw data acquired from breast lesions, we reconstruct B-mode images using different compression levels. Next, transfer learning is applied for classification. Differently reconstructed images are employed for training and evaluation. We show that the modification of the reconstruction algorithm leads to decrease of classification performance. As a remedy, we propose a method of data augmentation. We show that the augmentation of the training set with differently reconstructed B-mode images leads to a more robust and efficient classification. Our study suggests that it is important to take into account image reconstruction algorithms implemented in medical scanners during development of computer aided diagnosis systems.
Tumor Saliency Estimation for Breast Ultrasound Images via Breast Anatomy Modeling
Utah State University（犹他州立大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Idaho（爱达荷大学）；The Affiliated Hospital of Qingdao University（青岛大学附属医院）；The Second Hospital of Hebei Medical University（河北医科大学第二医院）
Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI
School of Information Technology and Electrical Engineering, University of Queensland（昆士兰大学信息技术与电气工程学院）；Science and Engineering Faculty, Queensland University of Technology（昆士兰科技大学科学与工程学院）
Model Agnostic Saliency for Weakly Supervised Lesion Detection from Breast DCE-MRI
The University of Adelaide（阿德莱德大学）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；Delft University of Technology（代尔夫特理工大学）；Queensland University of Technology（昆士兰科技大学）
Robustness of breast lesion segmentation under MRI undersampling improves with k-space-aware deep learning
Institute for AI in Medicine (IKIM), University Hospital Essen（人工智能医学研究所（IKIM），埃森大学医院）；Department of Physics, Technical University Dortmund（物理系，多特蒙德技术大学）；Division of Radiology, German Cancer Research Center (DKFZ)（放射学部，德国癌症研究中心（DKFZ））；Cancer Research Center Cologne Essen (CCCE), University Medicine Essen（科隆埃森癌症研究中心（CCCE），埃森大学医学中心）；RACOON Study Group, Site Essen（RACOON研究组，埃森站点）；German Cancer Consortium (DKTK), Partner Site Essen（德国癌症联合会（DKTK），埃森合作伙伴站点）；Medical Faculty and Faculty of Computer Science, University of Duisburg-Essen（医学系和计算机科学系，杜伊斯堡-埃森大学）
LesiOnTime -- Joint Temporal and Clinical Modeling for Small Breast Lesion Segmentation in Longitudinal DCE-MRI
Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria（医学影像研究实验室，生物医学影像与影像引导治疗系，维也纳医科大学）；Comprehensive Center for AI in Medicine, Medical University of Vienna, Austria（医学人工智能综合中心，维也纳医科大学）；Breast Health Center, Privatklinik Döbling, Austria（乳腺健康中心，多克林根私人诊所）；Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria（生物医学影像与影像引导治疗系，维也纳医科大学）；Department of Obstetrics and Gynecology, Medical University of Vienna, Austria（妇产科系，维也纳医科大学）；Department of Pathology, Medical University of Vienna, Austria（病理学系，维也纳医科大学）；Comprehensive Cancer Center, Medical University of Vienna, Austria（综合癌症中心，维也纳医科大学）
Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge
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）；National University of Singapore（新加坡国立大学）；Shenzhen University（深圳大学）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；Helmholtz Imaging（亥姆霍兹成像机构）
Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities
Shanghai Institute for Advanced Communication and Data Science（上海先进通信与数据科学研究院）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；Fudan University Shanghai Cancer Center（复旦大学附属肿瘤医院）；Shanghai Medical College, Fudan University（复旦大学上海医学院）；School of Science, Shanghai University（上海大学理学院）
UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis
School of Engineering, Tokyo Institute of Technology（东京技术大学工学院）；Faculty of Information Technology Nguyen Tat Thanh University（Nguyen Tat Thanh大学信息科技学院）
Deep Learning Predicts Mammographic Breast Density in Clinical Breast Ultrasound Images
University of Hawai’i at Mānoa（夏威夷大学马诺阿分校）；University of Hawai’i Cancer Center（夏威夷大学癌症中心）；The Queen’s Medical Center（皇后医疗中心）；Hawaiʻi Pacific Health（夏威夷太平洋健康）；Hawaiʻi Diagnostic Radiology Services（夏威夷诊断放射服务中心）
CEIMVEN: An Approach of Cutting Edge Implementation of Modified Versions of EfficientNet (V1-V2) Architecture for Breast Cancer Detection and Classification from Ultrasound Images
KaleidoSoft；IUBAT- International University of Business Agriculture and Technology（IUBAT-国际商业农业与技术大学）
A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories
Peking University（北京大学）；Peking Union Medical College Hospital（北京大学人民医院）；Peking University Cancer Hospital & Institute（北京大学肿瘤医院）；National Cancer Center / National Clinical Research Center for Cancer / Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院国家癌症中心/国家癌症临床科研中心/癌症医院）；Shenzhen Maternity & Child Health Care Hospital（深圳妇幼保健医院）；Xi’an Jiaotong University（西安交通大学）；Yizhun Medical AI Co., Ltd（亿众医疗人工智能有限公司）
Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results
The Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）；Sidney Kimmel Comprehensive Cancer Center（西德尼·基梅尔综合癌症中心）；The Johns Hopkins University（约翰霍普金斯大学）；University of Kentucky（肯塔基大学）；University of Wisconsin School of Medicine and Public Health（威斯康星大学医学与公共卫生学院）
OpenBreastUS: Benchmarking Neural Operators for Wave Imaging Using Breast Ultrasound Computed Tomography
Peking University（北京大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）；Yanqi Lake Beijing Institute of Mathematical Sciences and Applications（雁栖湖北京数学科学与应用研究院）；DP Technology（深势科技）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）
Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides
Norwegian University of Science and Technology (NTNU)（挪威科技大学）；St. Olavs hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）；SINTEF Digital（SINTEF数字研究所）；Sopra Steria（索普瑞斯特利亚）；Uppsala University（乌普萨拉大学）；University of Bergen（卑尔根大学）；Haukeland University Hospital（豪克兰大学医院）
Towards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation
Department of Computer Engineering, Kwame Nkrumah University of Science and Technology（计算机工程系，加纳科拉布大学科学与技术大学）；Global Health and Infectious Disease Group, Kumasi Centre for Collaborative Research in Tropical Medicine（全球卫生与传染病组，科拉布热带医学协作研究中心）；Department of Radiology, National Hospital Abuja（放射科，阿布贾国家医院）；Computer Science Department, FAST National University of Computer and Emerging Sciences（计算机科学系，FAST国家计算机与新兴科学大学）；Medical Artificial Intelligence Lab, Lagos, Nigeria（医学人工智能实验室，尼日利亚拉各斯）；Department of Biomedical Engineering, McGill University（生物医学工程系，麦吉尔大学）；Perelman School of Medicine, University of Pennsylvania（佩尔曼医学学院，宾夕法尼亚大学）；Nnamdi Azikiwe University Teaching Hospital（纳曼迪·阿齐基韦大学教学医院）
Sustainable Deep Learning-Based Breast Lesion Segmentation: Impact of Breast Region Segmentation on Performance
Norwegian University of Science and Technology（挪威科技大学）；More og Romsdal Hospital Trust（默勒-鲁姆斯达尔医院信托基金）；Stavanger University Hospital（斯塔万格大学医院）；The University of Stavanger（斯塔万格大学）；Akershus University Hospital（阿克什胡斯大学医院）；University of Oslo（奥斯陆大学）
A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos
Xiamen University（厦门大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究院）；The Hong Kong Polytechnic University（香港理工大学）
Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images
The Chinese University of Hong Kong（香港中文大学）；University of Cambridge（剑桥大学）；Inception Institute of Artificial Intelligence（启迪人工智能研究院）；The Hong Kong University of Science and Technology（香港科技大学）；Jinan University（暨南大学）；Southern University of Science and Technology（南方科技大学）；Chinese Academy of Sciences（中国科学院）
BUSIS: A Benchmark for Breast Ultrasound Image Segmentation
Harbin Institute of Technology（哈尔滨工业大学）；University of Idaho（爱达荷大学）；Utah State University（犹他州立大学）；Affiliated Hospital of Medical College Qingdao University（青岛大学医学院附属医院）；Second Hospital of Hebei Medical University（河北医科大学第二医院）
