Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学部生物医学工程学院）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州研究院）；Army Medical University (Third Military Medical University)（中国人民解放军陆军军医大学（第三军医大学））；The First Affiliated Hospital, College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）；College of Information Science and Engineering, Ritsumeikan University（立命馆大学信息科学与工程学院）；University of Science and Technology of China（中国科学技术大学）
Medical image segmentation is crucial in the field of medical imaging, aiding in disease diagnosis and surgical planning. Most established segmentation methods rely on supervised deep learning, in which clean and precise labels are essential for supervision and significantly impact the performance of models. However, manually delineated labels often contain noise, such as missing labels and inaccurate boundary delineation, which can hinder networks from correctly modeling target characteristics. In this paper, we propose a deep self-cleansing segmentation framework that can preserve clean labels while cleansing noisy ones in the training phase. To achieve this, we devise a gaussian mixture model-based label filtering module that distinguishes noisy labels from clean labels. Additionally, we develop a label cleansing module to generate pseudo low-noise labels for identified noisy samples. The preserved clean labels and pseudo-labels are then used jointly to supervise the network. Validated on a clinical liver tumor dataset and a public cardiac diagnosis dataset, our method can effectively suppress the interference from noisy labels and achieve prominent segmentation performance.
Region-Normalized DPO for Medical Image Segmentation under Noisy Judges
Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany（人工智能医学研究所（IKIM）、埃森大学医院（AöR））；Department of Nuclear Medicine, University Hospital Essen (AöR), Essen, Germany（核医学系、埃森大学医院（AöR））；Department of Physics, TU Dortmund, Dortmund, Germany（物理系、多特蒙德技术大学）；Heidelberg University Hospital, Heidelberg, Germany（海德堡大学医院）
Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection
Division of Medical Image Computing, Germany Cancer Research Center（德国癌症研究中心医学图像计算部）；Medical Faculty, University of Heidelberg（海德堡大学医学院）；Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO)（海德堡放射肿瘤学研究所（HIRO），国家放射肿瘤学研究中心（NCRO））；Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital（海德堡大学医院放射肿瘤科模式分析与学习组）；Faculty of Mathematics and Computer Science, University of Heidelberg（海德堡大学数学与计算机科学学院）；National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and the university medical center Heidelberg（国家肿瘤疾病中心（NCT），NCT海德堡，DKFZ与海德堡大学医学中心的合作机构）
Structure and Progress Aware Diffusion for Medical Image Segmentation
School of Artificial Intelligence（人工智能学院）；School of Computer Science and Technology（计算机科学与技术学院）；State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology（光电信息采集与防护技术国家重点实验室）；Anhui Provincial Key Laboratory of Security Artificial Intelligence（安徽省安全人工智能重点实验室）；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）
From Noisy Labels to Intrinsic Structure: A Geometric-Structural Dual-Guided Framework for Noise-Robust Medical Image Segmentation
College of physics；information engineering, Fuzhou University, Xueyuan Road No.2, Fuzhou, 350108, China；Bioengineering Department；Imperial-X, Imperial College London, London W12 7SL, UK；Lung Institute, Imperial College London, London SW7 2AZ, UK；Cardiovascular Research Centre, Royal Brompton Hospital, London SW3 6NP, UK；School of Biomedical Engineering \& Imaging Sciences, King's College London, London WC2R 2LS, UK；Faculty of Applied Science, Macao Polytechnic University, Macao Special Administrative Region of China
ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation
Southern University of Science and Technology（南方科技大学）；University of Hong Kong（香港大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
Semi-Supervised Medical Image Segmentation via Dual Networks
AIPM, School of Artificial Intelligence, Guilin University of Electronic Technology（桂林电子科技大学人工智能学院AIPM）；College of Computer Science and Engineering, Jeddah University（吉达大学计算机科学与工程学院）
Hierarchical Attention Networks for Medical Image Segmentation
Renmin University of China（中国人民大学）；AInnovation Technology Co., Ltd.（创新奇智科技有限公司）；Northwestern Polytechnical University（西北工业大学）；Visionary Intelligence Ltd.（远景智能有限公司）；Beijing Tongren Hospital（北京同仁医院）；Peking Union Medical College Hospital（北京协和医院）
Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labeling
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China, 541004（桂林电子科技大学人工智能学院）；The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure, Montreal, Canada, H3C 1K3（影像、视觉与人工智能实验室）；College of Medicine, Taibah University, Al Madinah, Saudi Arabia, 42353（塔布大学医学院）；Department of Radiology, Prince Mohammed Bin Abdulaziz Hospital, Ministry of National Guard Health Affairs, Al Madinah, Saudi Arabia, 42324（王子穆罕默德·本·阿卜杜勒阿齐兹医院放射科）；College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia, 42353（塔布大学计算机科学与工程学院）；College of Computer Science and Engineering, Jeddah University, Jeddah, Saudi Arabia, 23445（吉达大学计算机科学与工程学院）
Generalizing Abstention for Noise-Robust Learning in Medical Image Segmentation
Institute of Computer Science, University of Bonn（博恩大学计算机科学研究所）；Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS（弗劳恩霍夫智能分析与信息系统研究所）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔人工智能与机器学习研究所）
Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook
Japan Advanced Institute of Science and Technology（日本科学技术先进研究院）；University College London（伦敦大学学院）；The Hong Kong University of Science and Technology（香港科学大学）；University of Toyama（toyama大学）；Monash University（墨尔本大学）
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Engineering, Hong Kong University of Science and Technology（香港科技大学工程学院）
Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation
Department of Electronic Engineering, Tsinghua University, Beijing, China（中国北京清华大学电子工程系）；Institute for Precision Medicine, Tsinghua University, Beijing, China（中国北京清华大学精准医学研究院）
How Bad is Good enough: Noisy annotations for instrument pose estimation
Technische Universität Darmstadt（达姆施塔特工业大学）
虽然通过深度学习对医学图像的分析在各种应用中取得了前所未有的结果，但很少以系统的方式研究\emph{noisy training annotations}的效果。在医学图像分析中，解决这个问题的大多数报告都集中在研究深度学习分类器的分割性能。在这些研究中缺乏连续的地面实况注释限制了应用结论的价值，其中回归是主要的选择方法。在手术器械姿态估计的应用中，精确度对患者结果有直接的临床影响，研究\emph{noisy annotations}对深度学习姿态估计技术的影响至关重要。由于无法获得地面实况注释，因此真实X射线图像不足以进行该评价。我们规避这个问题，通过生成合成的射线照片，其中地面真实姿态是已知的，因此可以从实验中估计由医学专家的姿态估计误差。此外，该研究显示了深度神经网络在回归设置中从具有足够数据的噪声注释中学习主导信号的特性。
Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data
University of Michigan-Shanghai Jiao Tong University Joint Institute（密歇根大学-上海交通大学联合学院）；Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学未来技术全球研究院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）
Annotation-efficient deep learning for automatic medical image segmentation
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Peng Cheng Laboratory（鹏城实验室）；Pazhou Laboratory（琶洲实验室）；Guangdong General Hospital, Guangdong Academy of Medical Sciences（广东省人民医院（广东省医学科学院））；Renmin Hospital of Wuhan University（武汉大学人民医院）；School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University（北京大学深圳研究生院电子与计算机工程学院）；Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院脑认知与脑疾病研究所）；ETS Montreal（蒙特利尔高等技术学院）
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?
Center for Imaging Science, RIT（罗切斯特理工学院成像科学中心）；University of Aberdeen（阿伯丁大学）；NepAl Applied Mathematics and Informatics Institute for Research (NAAMII)（尼泊尔应用数学与信息学研究所）；Biomedical Engineering, RIT（罗切斯特理工学院生物医学工程系）
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise
Center for Imaging Science, Rochester Institute of Technology（罗切斯特理工学院成像科学中心）；University of Aberdeen（阿伯丁大学）；Biomedical Engineering, Rochester Institute of Technology（罗切斯特理工学院生物医学工程系）
Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院高级医学计算与分析中心）；Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院戈登医学成像中心）
Bayesian Statistics Guided Label Refurbishment Mechanism: Mitigating Label Noise in Medical Image Classification
Peking University（北京大学）；Peking University Health Science Center（北京大学医学部）；Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Shenzhen Bay Laboratory（深圳湾实验室）；Beijing Cancer Hospital & Institute（北京肿瘤医院暨研究所）
