Medical image segmentation requires consensus ground truth segmentations to be derived from multiple expert annotations. A novel approach is proposed that obtains consensus segmentations from experts using graph cuts (GC) and semi supervised learning (SSL). Popular approaches use iterative Expectation Maximization (EM) to estimate the final annotation and quantify annotator's performance. Such techniques pose the risk of getting trapped in local minima. We propose a self consistency (SC) score to quantify annotator consistency using low level image features. SSL is used to predict missing annotations by considering global features and local image consistency. The SC score also serves as the penalty cost in a second order Markov random field (MRF) cost function optimized using graph cuts to derive the final consensus label. Graph cut obtains a global maximum without an iterative procedure. Experimental results on synthetic images, real data of Crohn's disease patients and retinal images show our final segmentation to be accurate and more consistent than competing methods.
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）
FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging
Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM)（巴塞罗那医学人工智能实验室（BCN-AIM））；Maggioli SPA（马焦利股份公司）；Fundació TIC Salut i Social（信息与通信技术社会基金会）；Generalitat de Catalunya（加泰罗尼亚政府）；Hospital Universitario y Politécnico La Fe（拉费大学及综合医院）；Health Research Institute（健康研究所）；Foundation for Research and Technology（研究与技术基金会）；National Research Council（国家研究委员会）；Maastricht University（马斯特里赫特大学）
RSCFed: Random Sampling Consensus Federated Semi-supervised Learning
The Hong Kong University of Science and Technology（香港科技大学）；IHPC, A*STAR（新加坡科技研究局高性能计算研究所）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Impact of individual rater style on deep learning uncertainty in medical imaging segmentation
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal（蒙特利尔理工学院生物医学工程研究所NeuroPoly实验室）；Mila - Quebec AI Institute（Mila-魁北克人工智能研究所）；Functional Neuroimaging Unit, CRIUGM, Université de Montréal（蒙特利尔大学CRIUGM功能神经影像单元）
Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation
Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（北京协和医院，中国医学科学院北京协和医学院）；Beijing University of Posts and Telecommunications（北京邮电大学）；Nanyang Technological University（南洋理工大学）；Ant Group（蚂蚁集团）；Shanghai Innovation Institute（上海创新研究院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Fourth Military Medical University（中国人民解放军空军军医大学）；Macao Polytechnic University（澳门理工大学）；Wuhan University（武汉大学）；The Hong Kong Polytechnic University（香港理工大学）
DICE: Diffusion Consensus Equilibrium for Sparse-view CT Reconstruction
Department of Systems and Informatics Engineering（系统与信息工程系）；Department of Electrical, Electronics, and Telecommunications Engineering（电气、电子与电信工程系）；Department of Physics（物理系）；Universidad Industrial de Santander（圣安德烈斯工业大学）
CASC-AI: Consensus-aware Self-corrective Learning for Noise Cell Segmentation
Weill Cornell Medicine（威尔康奈尔医学院）；Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；University of Florida（佛罗里达大学）；Cornell Tech（康奈尔科技学院）
DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model
West China Biomedical Big Data Center（西昌生物医学大数据中心）；West China Hospital（西昌医院）；Sichuan University（四川大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
ProSona: Prompt-Guided Personalization for Multi-Expert Medical Image Segmentation
University Hospital Bonn（波恩大学医院）；LMU University Hospital（慕尼黑莱茵河医学院医院）；University Hospital Düsseldorf（杜伊斯堡大学医院）；Heidelberg University Hospital（海德堡大学医院）；Università Cattolica del Sacro Cuore（罗马天主教圣心大学）；University Hospital Zurich（苏黎世大学医院）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；DTU Compute（技术大学计算学院）；Helmholtz AI（海德堡中心人工智能）
Performance uncertainty in medical image analysis: a large-scale investigation of confidence intervals
Sorbonne Université（索邦大学）；Institut du Cerveau – Paris Brain Institute - ICM（脑研究所—巴黎脑研究所—ICM）；CNRS（国家科学研究中心）；Inria（法国国家信息与自动化技术研究院）；Inserm（法国国家医学研究院）；AP-HP（法国国家医院集团）；Hôpital de la Pitié-Salpêtrière（皮蒂埃-萨尔普特里医院）；German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；National Center for Tumor Diseases (NCT)（肿瘤国家研究中心（NCT））；AI Health Innovation Cluster（人工智能健康创新集群）；Unit for Lifelong Health and Ageing at UCL（伦敦大学学院（UCL）长寿健康单位）；Department of Population Science and Experimental Medicine and Hawkes InstituteCentre for Medical Image Computing（人口科学与实验医学系及哈维斯研究所医学影像计算中心）；Department of Computer Science, University College London（伦敦大学学院（UCL）计算机科学系）；School of Biomedical Engineering and Imaging Science, King’s College London（伦敦国王学院生物医学工程与影像科学学校）；Hawkes Institute, Department of Computer Science, University College London（哈维斯研究所，伦敦大学学院（UCL）计算机科学系）
Harmonized Feature Conditioning and Frequency-Prompt Personalization for Multi-Rater Medical Segmentation
Faculty of Informatics and Data Science, University of Regensburg（信息学院与数据科学学院，莱比锡大学）；Sharif University of Technology（谢赫·伊斯兰大学）；Iran University of Science and Technology（伊朗科学技术大学）；ARTORG Center for Biomedical Research, University of Bern（伯尔尼大学生物医学研究中心）；Department of Radiation Oncology, University Hospital Bern, University of Bern（伯尔尼大学放射肿瘤科，伯尔尼大学）；Fraunhofer Institute for Digital Medicine MEVIS, Bremen（弗劳恩霍夫数字医学研究所MEVIS，不莱梅）
Multi-rater Prompting for Ambiguous Medical Image Segmentation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Institute of Wenzhou, Zhejiang University（浙江大学温州研究院）；State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院第二附属医院血管植入装置国家重点实验室）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Notre Dame（圣母大学）；School of Public Health Zhejiang University（浙江大学公共卫生学院）
MedCoT: Medical Chain of Thought via Hierarchical Expert
ZJU-Angelalign R&D Center for Intelligence Healthcare, Zhejiang University（浙江大学ZJU-Angelalign智能医疗研发中心）；Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局前沿人工智能研究中心）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）
Dual Teacher-Student Learning for Semi-supervised Medical Image Segmentation
Center for Applied Mathematics, Tianjin University（应用数学中心，天津大学）；Department of Radiology, Tianjin Hospital of Tianjin University（放射科，天津大学医院）；Department of Hip Trauma, Tianjin Hospital of Tianjin University（骨科创伤科，天津大学医院）
Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation
Nanjing University（南京大学）；Southeast University（东南大学）；State Key Laboratory for Novel Software Technology（计算机软件新技术国家重点实验室）；National Institute of Healthcare Data Science（国家医疗数据科学研究院）
Concept-to-Pixel: Prompt-Free Universal Medical Image Segmentation
School of Biomedical Engineering, Division of Life Sciences；Medicine, University of Science；Technology of China (USTC), Hefei, Anhui 230026, China Center for Medical Imaging, Robotics, Analytic Computing \& Learning (MIRACLE), Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu 215123, China Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology, Suzhou Jiangsu, 215123, China State Key Laboratory of Precision
FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation
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 AI laboratory（上海人工智能实验室）；West China Hospital, Sichuan University（四川大学华西医院）；West China Biomedical Big Data Center, West China Hospital, Sichuan University（四川大学华西医院华西生物医学大数据中心）；SenseTime Research（商汤科技研究院）
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
Iran University of Science and Technology（伊朗科技大学）；RWTH Aachen University（亚琛工业大学）；Shahid Beheshti University（沙希德·贝赫什提大学）；University of Montreal（蒙特利尔大学）；Polytechnique Montreal（蒙特利尔理工学院）；Quebec AI Institute（魁北克人工智能研究所）；University of Regensburg（雷根斯堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）
Expert-aware uncertainty estimation for quality control of neural-based blood typing
Institute for Information Transmission Problems（信息传输问题研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；The Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；AIRI（AIRI（人工智能研究机构））
Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Net
Harbin Institute of Technology at Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng Lab（鹏城实验室）；MindsGo Co.Ltd（MindsGo 有限公司）；Capital Medical University（首都医科大学）；Xuanwu Hospital Capital Medical University（首都医科大学宣武医院）
TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation
RWTH Aachen University（亚琛工业大学）；Iran University of Science and Technology（伊朗科技大学）；Tehran University of Medical Sciences（德黑兰医科大学）；Shahid Beheshti University（沙希德·贝赫什提大学）；Stanford University（斯坦福大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）
On the Degrees of Freedom of Gridded Control Points in Learning-Based Medical Image Registration
UCL Hawkes Institute（UCL霍克斯研究所；医学物理与生物医学工程系，伦敦大学学院）；Department of Medical Physics and Biomedical Engineering, University College London（医学成像中心，医学系，伦敦大学学院）；Centre for Medical Imaging, Division of Medicine, University College London（外科与介入科学系，伦敦大学学院）；Division of Surgery and Interventional Science, University College London（癌症研究所，泌尿科，UCL医院，伦敦大学学院）；Cancer Institute, Urology Department, UCL Hospital, University College London（放射科，帝国理工医疗）；Radiology Department, Imperial College Healthcare
