Anomaly segmentation, which localizes defective areas, is an important component in large-scale industrial manufacturing. However, most recent researches have focused on anomaly detection. This paper proposes a novel anomaly segmentation network (AnoSeg) that can directly generate an accurate anomaly map using self-supervised learning. For highly accurate anomaly segmentation, the proposed AnoSeg considers three novel techniques: Anomaly data generation based on hard augmentation, self-supervised learning with pixel-wise and adversarial losses, and coordinate channel concatenation. First, to generate synthetic anomaly images and reference masks for normal data, the proposed method uses hard augmentation to change the normal sample distribution. Then, the proposed AnoSeg is trained in a self-supervised learning manner from the synthetic anomaly data and normal data. Finally, the coordinate channel, which represents the pixel location information, is concatenated to an input of AnoSeg to consider the positional relationship of each pixel in the image. The estimated anomaly map can also be utilized to improve the performance of anomaly detection. Our experiments show that the proposed method outperforms the state-of-the-art anomaly detection and anomaly segmentation methods for the MVTec AD dataset. In addition, we compared the proposed method with the existing methods through the intersection over union (IoU) metric commonly used in segmentation tasks and demonstrated the superiority of our method for anomaly segmentation.
Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative
The University of Queensland（昆士兰大学）；Australian e-Health Research Centre（澳大利亚电子健康研究中心）；Commonwealth Scientific and Industrial Research Organisation（联邦科学与工业研究组织）
Dr-SAM: An End-to-End Framework for Vascular Segmentation, Diameter Estimation, and Anomaly Detection on Angiography Images
Yerevan State University (YSU)（埃里温国立大学）；Ruhr-Universität Bochum (RUB)（波鸿鲁尔大学）；Synopsys Armenia CJSC（新思亚美尼亚公司）；ServiceTitan, Inc.（ServiceTitan公司）；Picsart AI Research (PAIR)（Picsart人工智能研究院）；Johannes Wesling University Hospital（约翰内斯·韦斯林大学医院）
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images
Univ. Rennes（雷恩大学）；INSA Rennes（雷恩国立应用科学学院）；CNRS（法国国家科学研究中心）；University Hospital Bonn（波恩大学医院）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；Technical University of Munich（慕尼黑工业大学）
3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI
Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology（汉堡工业大学医学技术与智能系统研究所）；jung diagnostics GmbH（jung diagnostics 有限公司）
Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images
Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany（技术大学慕尼黑计算机辅助医疗程序）；Munich Center of Machine Learning, Munich, Germany（慕尼黑机器学习中心）；Department for Vascular and Endovascular Surgery, rechts der Isar University Hospital, Technical University of Munich, Munich, Germany（技术大学慕尼黑血管和内血管外科部门）
Looking at the whole picture: constrained unsupervised anomaly segmentation
Institute of Transport and Territory（交通与领土研究所）；Universitat Politècnica de València（瓦伦西亚理工大学）；LIVIA Laboratory（LIVIA实验室）；École de Technologie Supérieure (ETS)（高等技术学院）
Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition
Khalifa University（哈利法大学）；Durham University（杜伦大学）；The University of Western Australia（西澳大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models
School of Electrical and Computer Engineering, University of Oklahoma（俄克拉荷马大学电气与计算机工程学院）；University of Oklahoma Health Sciences Center（俄克拉荷马大学健康科学中心）；School of Computer Science, University of Oklahoma（俄克拉荷马大学计算机科学学院）
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT
Medical University Vienna（维也纳医科大学）；University of Vienna（维也纳大学）；NVIDIA Corporation（英伟达公司）
通过检测医学图像中的相关生物标志物来辅助诊断和治疗指导。虽然监督式深度学习可以对病理区域进行准确分割，但它受到限制，需要对这些区域进行先验定义，大规模注释以及训练集中的代表性患者队列。相反，异常检测不限于病理的特定定义，并且允许在没有注释的情况下对健康样本进行训练。然后，异常区域可以作为生物标志物发现的候选者。关于正常解剖结构的知识带来了用于检测异常的隐含信息。我们建议使用baidu深度学习来利用这一特性，基于这样的假设，即认知的不确定性将与正常训练集的解剖偏差相关。贝叶斯U形网是在一个定义良好的健康环境中训练的，使用现有方法产生的健康解剖结构的弱标签。在测试时，我们使用Monte Carlo dropout捕获模型的认知不确定性估计。一种新的后处理技术，然后应用到利用这些估计，并将其分层的外观平滑斑点形状的分割的异常。我们实验验证了这种方法在视网膜光学相干断层扫描（OCT）图像，使用弱标签的视网膜层。我们的方法在年龄相关性黄斑变性（AMD）病例的独立异常测试集中获得了0.789的Dice指数。所得到的分割允许非常高的准确性，以分离健康和患病的情况下，晚期湿性AMD，干地图状萎缩（GA），糖尿病性黄斑水肿（DME）和视网膜静脉阻塞（RVO）。最后，我们定性地观察到，我们的方法也可以检测正常扫描中的其他偏差，如切割边缘伪影。
Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Models
H. Milton Stewart School of Industrial and Systems Engineering（H. Milton Stewart工业与系统工程学院）；Georgia Institute of Technology（佐治亚理工学院）；Department of Mechanical Engineering（机械工程系）；Polytechnic University of Milan（米兰理工学院）
Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images
Harvard Medical School（哈佛医学院）；Australian Institute for Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；University of Adelaide（阿德莱德大学）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）；Singapore Management University（新加坡管理大学）；University of Surrey（萨里大学）
MD Loss: Efficient Training of 3D Seismic Fault Segmentation Network under Sparse Labels by Weakening Anomaly Annotation
College of computer science and technology, China University of Petroleum (East China)（中国石油大学（华东）计算机科学与技术学院）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；Shengli Oilfield Company, SINOPEC（中国石化胜利油田分公司）
Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection
Technical University of Munich（慕尼黑工业大学）；Helmholtz AI（亥姆霍兹人工智能）；Helmholtz Center Munich（亥姆霍兹慕尼黑中心）；Klinikum Rechts der Isar（伊萨尔河右岸医院）；Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
King’s College London（伦敦国王学院）；University College London（伦敦大学学院）；Birkbeck College（伯克贝克学院）；King’s College Hospital NHS Foundation Trust（国王学院医院国民保健服务基金会信托）；St George’s University Hospitals NHS Foundation Trust（圣乔治大学医院国民保健服务基金会信托）；Atkinson Morley Regional Neuroscience Centre（阿特金森·莫利地区神经科学中心）；UCL Queen Square Institute of Neurology（伦敦大学学院皇后广场神经病学研究所）
