Nodule-DETR: A Novel DETR Architecture with Frequency-Channel Attention for Ultrasound Thyroid Nodule Detection
Beihang University（北京航空航天大学）；National Cancer Center（国家癌症中心）；National Clinical Research Center for Cancer（国家癌症临床医学研究中心）；Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院肿瘤医院）；Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University（北京航空航天大学生物医学工程高精尖创新中心）
Jingjing Wang, Qianglin Liu, Zhuo Xiao, Xinning Yao, Bo Liu, Lu Li, Lijuan Niu, Fugen Zhou
Thyroid cancer is the most common endocrine malignancy, and its incidence is rising globally. While ultrasound is the preferred imaging modality for detecting thyroid nodules, its diagnostic accuracy is often limited by challenges such as low image contrast and blurred nodule boundaries. To address these issues, we propose Nodule-DETR, a novel detection transformer (DETR) architecture designed for robust thyroid nodule detection in ultrasound images. Nodule-DETR introduces three key innovations: a Multi-Spectral Frequency-domain Channel Attention (MSFCA) module that leverages frequency analysis to enhance features of low-contrast nodules; a Hierarchical Feature Fusion (HFF) module for efficient multi-scale integration; and Multi-Scale Deformable Attention (MSDA) to flexibly capture small and irregularly shaped nodules. We conducted extensive experiments on a clinical dataset of real-world thyroid ultrasound images. The results demonstrate that Nodule-DETR achieves state-of-the-art performance, outperforming the baseline model by a significant margin of 0.149 in mAP@0.5:0.95. The superior accuracy of Nodule-DETR highlights its significant potential for clinical application as an effective tool in computer-aided thyroid diagnosis. The code of work is available at https://github.com/wjj1wjj/Nodule-DETR.
The ability to explain the prediction of deep learning models to end-users is an important feature to leverage the power of artificial intelligence (AI) for the medical decision-making process, which is usually considered non-transparent and challenging to comprehend. In this paper, we apply state-of-the-art eXplainable artificial intelligence (XAI) methods to explain the prediction of the black-box AI models in the thyroid nodule diagnosis application. We propose new statistic-based XAI methods, namely Kernel Density Estimation and Density map, to explain the case of no nodule detected. XAI m…
Frontline Intelligent Technology (Nanjing) Co.,Ltd.（南京锋线智能科技有限公司）；Xiangya Hospital, Central South University（中南大学湘雅医院）
Ultrasound examination is widely used in the clinical diagnosis of thyroid nodules (benign/malignant). However, the accuracy relies heavily on radiologist experience. Although deep learning techniques have been investigated for thyroid nodules recognition. Current solutions are mainly based on static ultrasound images, with limited temporal information used and inconsistent with clinical diagnosis. This paper proposes a novel method for the automated recognition of thyroid nodules through an exhaustive exploration of ultrasound videos and key-frames. We first propose a detection-localization f…
Anatomy-Guided Representation Learning Using a Transformer-Based Network for Thyroid Nodule Segmentation in Ultrasound Images
1 Department of Computer Science, Hanyang University, Seoul, 04762, South Korea；2 Department of Computer Science, The University of Alabama, Seoul, 04762, South Korea；3 Department of Biomedical Sciences, Seoul National University, Seoul, 08826, South Korea ( )；4 Department of Anesthesiology, Perioperative；Pain Medicine, Stanford University, CA 94305, USA ( )；5 Department of Computer Science, Hanyang University, Seoul, 04762, South Korea ( )；6 Department of Computer Engineering, Qatar University, Doha, Qatar ( )
AI-Generated Content Enhanced Computer-Aided Diagnosis Model for Thyroid Nodules: A ChatGPT-Style Assistant
Zhejiang Cancer Hospital（浙江省肿瘤医院）；Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences（中国科学院杭州医学研究所）；Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology（浙江省癌症智能诊断与分子技术研究中心）；Wenling Medical Big Data and Artificial Intelligence Research Institute（温岭医学大数据与人工智能研究院）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital)（浙江省肿瘤医院台州院区（台州市肿瘤医院））
College of Electronic and Information Engineering of Hebei University（河北大学电子信息工程学院）
The incidence of thyroid nodule is very high and generally increases with the age. Thyroid nodule may presage the emergence of thyroid cancer. The thyroid nodule can be completely cured if detected early. Fine needle aspiration cytology is a recognized early diagnosis method of thyroid nodule. There are still some limitations in the fine needle aspiration cytology, and the ultrasound diagnosis of thyroid nodule has become the first choice for auxiliary examination of thyroid nodular disease. If we could combine medical imaging technology and fine needle aspiration cytology, the diagnostic rate…
Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Sun Yat-sen University（中山大学）；Southern Medical University（南方医科大学）；Zhujiang Hospital（珠江医院）
Thyroid nodule classification aims at determining whether the nodule is benign or malignant based on a given ultrasound image. However, the label obtained by the cytological biopsy which is the golden standard in clinical medicine is not always consistent with the ultrasound imaging TI-RADS criteria. The information difference between the two causes the existing deep learning-based classification methods to be indecisive. To solve the Inconsistent Label problem, we propose an Adaptive Curriculum Learning (ACL) framework, which adaptively discovers and discards the samples with inconsistent lab…
A Deep Learning Framework for Thyroid Nodule Segmentation and Malignancy Classification from Ultrasound Images
Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK（沃里克大学计算机科学系组织图像分析中心，英国）；Royal Stoke University Hospital, University Hospitals of North Midlands, UK（北中部大学医院皇家斯托医院，英国）
Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training
School of Biomedical Engineering, Health Science Center, Shenzhen University（深圳大学健康科学中心生物医学工程学院）；Marshall Laboratory of Biomedical Engineering, Shenzhen University（深圳大学马歇尔生物医学工程实验室）；Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University（深圳大学医学超声图像计算实验室）；Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine（上海交通大学医学院附属瑞金医院超声科）；Shenzhen RayShape Medical Technology Co., Ltd（深圳RayShape医疗科技有限公司）
Prototype-Enhanced Multi-View Learning for Thyroid Nodule Ultrasound Classification
College of Software, Jilin University（吉林大学软件学院）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；School of Computing Technologies, RMIT University（RMIT大学计算技术学院）；School of Software Technology, Dalian University of Technology（大连理工大学软件学院）；Guangdong Institute of Smart Education, Jinan University（暨南大学广东智慧教育研究院）
Beyond Point Annotation: A Weakly Supervised Network Guided by Multi-Level Labels Generated from Four-Point Annotation for Thyroid Nodule Segmentation in Ultrasound Image
Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China（机器人科学与工程学院，东北大学，沈阳，中国）；Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada（生物医学工程系，萨斯喀彻温大学，萨斯喀彻温，加拿大）；Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China（超声科，中国医科大学附属盛京医院，沈阳，中国）
Weakly Supervised Segmentation Framework for Thyroid Nodule Based on High-confidence Labels and High-rationality Losses
Faculty of Robot Science and Engineering, Northeastern University（机器人科学与工程学院，东北大学）；Harbin Institute of Technology（哈尔滨工业大学）；Department of Ultrasound, Shengjing Hospital of China Medical University（中国医科大学附属盛京医院超声科）
CLIP-TNseg: A Multi-Modal Hybrid Framework for Thyroid Nodule Segmentation in Ultrasound Images
School of Electronic and Information, Beihang University（北京航空航天大学电子信息学院）；Peking University First Hospital（北京大学第一医院）；Unmanned System Research Institute, Beihang University（北京航空航天大学无人系统研究院）
Image Processing Center, Beihang University（北京航空航天大学图像处理中心）；State Key Laboratory of High-Efficiency Reusable Aerospace Transportation Technology（高效可重复使用航天运输技术国家重点实验室）；Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院肿瘤医院超声科）；State Key Laboratory of Virtual Reality Technology and Systems, Ministry of Education（虚拟现实技术与系统国家重点实验室）；the Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technology, Ministry of Education（航天器设计优化与动态仿真技术重点实验室）
Thyroid cancer is common worldwide, with a rapid increase in prevalence across North America in recent years. While most patients present with palpable nodules through physical examination, a large number of small and medium-sized nodules are detected by ultrasound examination. Suspicious nodules are then sent for biopsy through fine needle aspiration. Since biopsies are invasive and sometimes inconclusive, various research groups have tried to develop computer-aided diagnosis systems. Earlier approaches along these lines relied on clinically relevant features that were manually identified by …
Multistep Automated Data Labelling Procedure (MADLaP) for Thyroid Nodules on Ultrasound: An Artificial Intelligence Approach for Automating Image Annotation
Duke University（杜克大学）；Duke University Medical Center（杜克大学医学中心）
An Approach for Thyroid Nodule Analysis Using Thermographic Images
Computer Science Department, Universidade Federal Fluminense（联邦弗里蒙特大学计算机科学系）；Radiology Department, Hospital Universitário Antônio Pedro (HUAP)（安东尼奥佩德罗大学医院放射科）；Applied Computation Group NCA-UFMA, Universidade Federal do Maranhão（马兰舍大学应用计算组NCA-UFMA）
Calibrated Selective Prediction Using Deep Ensembles for ROI-Based Thyroid Nodule Ultrasound Classification Under Dataset Shift: A Retrospective Evaluation
Daffodil International University（达福德国际大学）；Birmingham City University（伯明翰城市大学）
STACT-Time: Spatio-Temporal Cross Attention for Cine Thyroid Ultrasound Time Series Classification
Medical \& Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles (UCLA), CA, USA Department of Bioengineering, UCLA, Los Angeles, CA, USA Department of Electrical；Computer Engineering, UCLA, Los Angeles, CA, USA Department of Radiological Sciences, UCLA, Los Angeles, CA, USA
Over the past decades, the incidence of thyroid cancer has been increasing globally. Accurate and early diagnosis allows timely treatment and helps to avoid over-diagnosis. Clinically, a nodule is commonly evaluated from both transverse and longitudinal views using thyroid ultrasound. However, the appearance of the thyroid gland and lesions can vary dramatically across individuals. Identifying key diagnostic information from both views requires specialized expertise. Furthermore, finding an optimal way to integrate multi-view information also relies on the experience of clinicians and adds fur…
Auditable agentic AI for evidence-grounded thyroid ultrasound diagnosis and reporting
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）；School of Mathematical Sciences, Zhejiang University（浙江大学数学科学学院）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Zhujiang Hospital, Southern Medical University（南方医科大学珠江医院）；College of Mathematical Medicine, Zhejiang Normal University（浙江师范大学数学医学院）
Multi-Kernel Gated Decoder Adapters for Robust Multi-Task Thyroid Ultrasound under Cross-Center Shift
Department of Astronomy and Physics, University of British Columbia（不列颠哥伦比亚大学天文与物理系）；Department of Basic and Translational Research, BC Cancer Research Institute（不列颠哥伦比亚癌症研究所在基本与转化研究部）；Department of Radiology, University of British Columbia（不列颠哥伦比亚大学放射学系）；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）
Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件学院）；Shaanxi Joint Key Laboratory for Artifact Intelligence（陕西省人工智能联合重点实验室）；School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电气工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of General Surgery, Xiangya Hospital, Central South University（中南大学湘雅医院普外科）；Department of Orthopaedics, Second Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第二附属医院骨科）；Hunan Frontline Medical Technology Co., Ltd.（湖南前沿医疗科技有限公司）
CPLOYO: A Pulmonary Nodule Detection Model with Multi-Scale Feature Fusion and Nonlinear Feature Learning
Tongji Hospital, School of Medicine, Tongji University（同济大学医学院附属同济医院）；Shanghai Pulmonology Hospital, School of Medicine, Tongji University（同济大学医学院附属上海肺科医院）；Carnegie Mellon University（卡内基梅隆大学）
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）
PADReg: Physics-Aware Deformable Registration Guided by Contact Force for Ultrasound Sequences
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Center for Artificial Intelligence and Robotics, HK Institute of Science & Innovation, Chinese Academy of Sciences（人工智能与机器人中心，香港创新科学院，中国科学院）；School of Biomedical Engineering and Imaging Sciences, King’s College London（生物医学工程与影像科学学院，伦敦国王学院）
