Anomaly Detection in Medical Imaging -- A Mini Review
Salzburg University of Applied Sciences（萨尔茨堡应用技术大学）；University of Salzburg（萨尔茨堡大学）
Maximilian E. Tschuchnig, Michael Gadermayr
备注：
Accepted and presented at iDSC2021 edit: During work on this publication Maximilian Ernst Tschuchnig was affiliated with Salzburg University of Applied Sciences and University of Salzburg
医学影像日益数字化，使基于机器学习的病变检测、可视化和分割改进成为可能，从而减轻医学专家的工作负担。然而，监督式机器学习需要可靠的标注数据，而这类数据往往难以或无法收集，至少非常耗时并因此成本高昂。因此，只需要部分标注数据的方法（半监督方法）或完全不需要标注的方法（无监督方法）得到了更频繁的应用。异常检测是一种可行的方法论，能够利用半监督和无监督方法来处理分类和分割等医学影像任务。本文通过对医学影像中相关异常检测论文进行半详尽的文献综述，将其按应用聚类，突出重要结果，总结经验教训，并就如何开展医学影像异常检测研究提供进一步建议。该定性分析基于 Google Scholar 和4个不同的搜索词，最终分析了120篇不同的论文。主要结果表明，当前研究的主要动机是减少对标注数据的需求。此外，脑部 MRI 领域中大量成功的研究显示了其在 OCT 和胸部 X 光等更多领域中的应用潜力。
英文摘要：
The increasing digitization of medical imaging enables machine learning based improvements in detecting, visualizing and segmenting lesions, easing the workload for medical experts. However, supervised machine learning requires reliable labelled data, which is is often difficult or impossible to collect or at least time consuming and thereby costly. Therefore methods requiring only partly labeled data (semi-supervised) or no labeling at all (unsupervised methods) have been applied more regularly. Anomaly detection is one possible methodology that is able to leverage semi-supervised and unsupervised methods to handle medical imaging tasks like classification and segmentation. This paper uses a semi-exhaustive literature review of relevant anomaly detection papers in medical imaging to cluster into applications, highlight important results, establish lessons learned and give further advice on how to approach anomaly detection in medical imaging. The qualitative analysis is based on google scholar and 4 different search terms, resulting in 120 different analysed papers. The main results showed that the current research is mostly motivated by reducing the need for labelled data. Also, the successful and substantial amount of research in the brain MRI domain shows the potential for applications in further domains like OCT and chest X-ray.
Towards Universal Unsupervised Anomaly Detection in Medical Imaging
Technical University of Munich（慕尼黑工业大学）；Helmholtz AI and Helmholtz Center Munich（亥姆霍兹人工智能与亥姆霍兹慕尼黑中心）；Klinikum Rechts der Isar（伊萨尔河右岸医院）；Imperial College London（伦敦帝国理工学院）
MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection
College of Information and Engineering, Capital Normal University（首都师范大学信息工程学院）；Peking University Third Hospital（北京大学第三医院）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images
Shanghai Academy of AI for Science University of Queensland（上海人工智能科学研究院 华南理工大学）；Fudan University Shanghai Academy of AI for Science（复旦大学 上海人工智能科学研究院）；University of Queensland（昆士兰大学）
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction
LPIXEL Inc.（LPIXEL公司）；University of Cambridge（剑桥大学）；National Institute of Informatics（国立信息学研究所）；National Center for Global Health and Medicine（国立国际医疗研究中心）；Juntendo University（顺天堂大学）；ELTE Eötvös Loránd University（厄特沃什·罗兰大学）；The University of Tokyo（东京大学）
Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images
ShanghaiTech University（上海科技大学）；Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Jiao Tong University（上海交通大学）；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）；Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局资讯通信研究院）；Southern University of Science and Technology（南方科技大学）；Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences（中国科学院慈溪生物医学工程研究所）
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）
MTL-MAD: Multi-Task Learners are Effective Medical Anomaly Detectors
Rayscape；University of Bucharest（布加勒斯特大学）；Polytechnic University of Timişoara（蒂米什瓦拉工业大学）；Colţea Clinical Hospital（科尔切亚医院）；Carol Davila University of Medicine and Pharmacy（卡罗尔·迪瓦ila医学院和药理学大学）
Realism in Action: Anomaly-Aware Diagnosis of Brain Tumors from Medical Images Using YOLOv8 and DeiT
Department of CS \& IT Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan, Iran；Department of Computer Engineering University of Zanjan (ZNU) Zanjan, Iran
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder
Harvard University（哈佛大学）；Singapore Management University（新加坡管理大学）；Australian Institute for Machine Learning（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）；University of Surrey（萨里大学）
Bias in Unsupervised Anomaly Detection in Brain MRI
Technical University of Munich（慕尼黑工业大学）；Helmholtz AI（亥姆霍兹人工智能研究所）；Helmholtz Center Munich（亥姆霍兹慕尼黑中心）；King’s College London（伦敦国王学院）；Klinikum Rechts der Isar（伊萨尔河畔临床教学中心）；Imperial College London（帝国理工学院）
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）
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images
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）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）；Faculty of Health and Medical Sciences, University of Adelaide（阿德莱德大学健康与医学科学学院）
I Detect What I Don't Know: Incremental Anomaly Learning with Stochastic Weight Averaging-Gaussian for Oracle-Free Medical Imaging
AI Research Lab, Department of Computer Science（人工智能研究实验室、计算机科学系）；Biomedical & Translational Sciences, Sanford School of Medicine University of South Dakota（生物医学与转化科学、桑福大学医学院）
Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images
The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；Institute of Medical Intelligence and XR, The Chinese University of Hong Kong（香港中文大学医学智能与扩展现实研究所）
Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection
Dept. of Engineering for Innovation Medicine, University of Verona, Italy（创新医学工程系，威尼斯大学，意大利）；School of Computer Science and Engineering, Beihang University, China（计算机科学与工程学院，北航大学，中国）；Dept. of Computer Science, Reykjavik University, Iceland（计算机科学系，雷克雅未克大学，冰岛）
