Self-supervised data curation provides a pathway to scaling and improving the generalization capabilities of machine learning models. By leveraging self-supervised learning (SSL) for data curation, the demand for massive training datasets required by foundation models can be effectively met. SSL greatly alleviates the costs associated with annotation and manual dataset curation while minimizing the need for human oversight. However, the integrity of SSL-curated datasets must be rigorously checked, as reliance on anonymous and unvetted external sources can substantially increase the risk of data poisoning. In this paper, we propose a Poisoned Data Detector (PDD), an active defense mechanism designed to ensure the integrity of SSL-curated datasets prior to foundation model training. PDDs are designed using a combination of the pretrained ImageBind model and traditional classifiers, including Random Forest (RF), k-Nearest Neighbors (KNN), Naive Bayes (NB), and Support Vector Machines (SVM). We rigorously evaluated PDDs using 176,200 images from three diverse datasets and three different adversarial attacks encompassing both in-distribution and out-of-distribution scenarios. Notably, SVM-PDD achieves superior performance for both in-distribution (Set3-Set5) and out-of-distribution (TrueFace and 140K RealFace) datasets. Our design demonstrates strong scalability and enables the rapid integration of new adversarial attack detectors through an ensemble approach.
Image-Based Breast Implant Detection for Mammography Dataset Curation and Near-Real-Time Deployment: Comparing Foundation Models and Task-Specific Convolutional Models
Emory University School of Medicine（埃默里大学医学院）；University of Maryland（马里兰大学）
Delineate Anything v2: A Global Foundation Model for Field Delineation
European Space Agency（欧洲航天局）；Space Research Institute NASU-SSAU（乌克兰国家科学院-乌克兰国家空间局空间研究所）；University of Maryland（马里兰大学）；National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”（乌克兰国立技术大学“ Igor Sikorsky基辅理工学院”）
Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology
Dept. of Computer Science, ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院计算机科学系）；AI Center, ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院人工智能中心）；Signal Processing Laboratory (LTS4), EPFL, Lausanne, Switzerland（日内瓦联邦理工学院信号处理实验室）；University of Basel, Basel, Switzerland（巴塞尔大学）；University Hospital of Basel, Basel, Switzerland（巴塞尔大学医院）；Idiap Research Institute, Martigny, Switzerland（伊迪普研究 institute）；Icahn School of Medicine at Mount Sinai, New York, United States（辛辛那提医学中心伊坎医学院）
OceanSAR-2: A Universal Feature Extractor for SAR Ocean Observation
Galeio, Paris, France（法国加利欧）；LOPS, Ifremer, Plouzané, France（法国若特默尔实验室）；OceanScope, Brest, France（法国海洋观测中心）；Nanjing University of Information Science and Technology, Nanjing, China（中国信息科学技术大学）
RudolfV: A Foundation Model by Pathologists for Pathologists
Aignostics, Germany（艾格诺斯蒂克斯公司）；Machine Learning Group, Technische Universität Berlin, Germany（柏林工业大学机器学习组）；BIFOLD – Berlin Institute for the Foundations of Learning and Data, Germany（柏林学习与数据基础研究所）；Department of Artificial Intelligence, Korea University, Republic of Korea（高丽大学人工智能系）；Max-Planck Institute for Informatics, Germany（马克斯·普朗克信息学研究所）；German Cancer Research Center (DKFZ) & German Cancer Consortium (DKTK), Berlin & Munich Partner Sites（德国癌症研究中心与德国癌症联盟柏林及慕尼黑合作站点）；Institute of Pathology, Ludwig-Maximilians-Universität München, Germany（慕尼黑大学病理学研究所）；Institute of Pathology, Charité – Universitätsmedizin Berlin, Germany（柏林夏里特医学院病理学研究所）；Bavarian Cancer Research Center (BZKF), Germany（巴伐利亚癌症研究中心）
DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Cellverse Co, Ltd.（Cellverse公司）；iHuman Institute, ShanghaiTech University（上海科技大学iHuman研究所）
A data- and compute-efficient chest X-ray foundation model beyond aggressive scaling
Stanford Center for Artificial Intelligence in Medicine and Imaging（斯坦福大学人工智能医学与成像中心）；Stanford University（斯坦福大学）；Department of Radiology（放射科）；Department of Computer Science（计算机科学系）；Department of Pediatrics（儿科）；Department of Biomedical Data Science（生物医学数据科学系）；Department of Medicine（医学系）
Lost in Distortion: Uncovering the Domain Gap Between Computer Vision and Brain Imaging -- A Study on Pretraining for Age Prediction
Center for Translational Research in Neuroimaging and Data Science（转化神经影像与数据科学研究中心）；College of Artificial Intelligence, Chengdu University of Information Technology（成都信息科技大学人工智能学院）；Department of Applied Mathematics and Statistics（应用数学与统计学系）；McWilliams School of Biomedical Informatics（麦卡威廉生物医学信息学学院）；Department of Applied Mathematics and Theoretical Physics（应用数学与理论物理系）；School of Biomedical Engineering and Imaging Sciences（生物医学工程与影像科学学院）
MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models
Yale University（耶鲁大学）；Korea University（韩国大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；The University of Queensland（昆士兰大学）；The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；University of Washington（华盛顿大学）；Microsoft Research（微软研究院）
