Anomaly detection (AD) aims at detecting abnormal samples that deviate from the expected normal patterns. Generally, it can be trained merely on normal data, without a requirement for abnormal samples, and thereby plays an important role in rare disease recognition and health screening in the medical domain. Despite the emergence of numerous methods for medical AD, the lack of a fair and comprehensive evaluation causes ambiguous conclusions and hinders the development of this field. To address this problem, this paper builds a benchmark with unified comparison. Seven medical datasets with five image modalities, including chest X-rays, brain MRIs, retinal fundus images, dermatoscopic images, and histopathology images, are curated for extensive evaluation. Thirty typical AD methods, including reconstruction and self-supervised learning-based methods, are involved in comparison of image-level anomaly classification and pixel-level anomaly segmentation. Furthermore, for the first time, we systematically investigate the effect of key components in existing methods, revealing unresolved challenges and potential future directions. The datasets and code are available at https://github.com/caiyu6666/MedIAnomaly.
TU Munich（慕尼黑工业大学）；ETH Zurich（苏黎世联邦理工学院）；Klinikum Rechts der Isar（Rechts der Isar 医院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these works is to learn a model of normal anatomy by learning to compress and recover healthy data. This allows to spot abnormal structures from erroneous recoveries of compressed, potentially anomalous samples. The concept is of great interest to the medical image analysis community as it i) relieves from the need of vast amounts of manually segmented training data---a necessity for and pitfall of current supervised Deep Lear…
Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imaging
Department of Computing, Imperial College London, UK（伦敦帝国理工学院计算机系）；Technical University Munich, DE（慕尼黑技术大学）；Munich Center for Machine Learning (MCML), DE（慕尼黑机器学习中心（MCML））
Video anomaly detection is one of the hot research topics in computer vision nowadays, as abnormal events contain a high amount of information. Anomalies are one of the main detection targets in surveillance systems, usually needing real-time actions. Regarding the availability of labeled data for training (i.e., there is not enough labeled data for abnormalities), semi-supervised anomaly detection approaches have gained interest recently. This paper introduces the researchers of the field to a new perspective and reviews the recent deep-learning based semi-supervised video anomaly detection a…
Harokopio University of Athens, Athens, Greece（雅典哈尔科波利欧大学）；National and Kapodistrian University of Athens, Athens, Greece（雅典国家与卡波迪斯蒂亚纳大学）；CentraleSupelec, University Paris Saclay, Paris, France（巴黎萨克雷中央苏普尔大学）；Archimedes, Athena Research Center, Athens, Greece（阿提卡研究所以及阿奇米德中心）
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Maryland（马里兰大学）
Ear related concerns and symptoms represents the leading indication for seeking pediatric healthcare attention. Despite the high incidence of such encounters, the diagnostic process of commonly encountered disease of the middle and external presents significant challenge. Much of this challenge stems from the lack of cost effective diagnostic testing, which necessitating the presence or absence of ear pathology to be determined clinically. Research has however demonstrated considerable variation among clinicians in their ability to accurately diagnose and consequently manage ear pathology. Wit…
Technical University of Munich（慕尼黑工业大学）；DeepC GmbH（DeepC有限公司）；Ludwig Maximilian University of Munich（慕尼黑路德维希·马克西米利安大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Medical imaging data suffers from the limited availability of annotation because annotating 3D medical data is a time-consuming and expensive task. Moreover, even if the annotation is available, supervised learning-based approaches suffer highly imbalanced data. Most of the scans during the screening are from normal subjects, but there are also large variations in abnormal cases. To address these issues, recently, unsupervised deep anomaly detection methods that train the model on large-sized normal scans and detect abnormal scans by calculating reconstruction error have been reported. In this…
AquaFusionNet: Lightweight VisionSensor Fusion Framework for Real-Time Pathogen Detection and Water Quality Anomaly Prediction on Edge Devices
Department of Informatics Engineering, Politeknik Negeri Banyuwangi（信息工程系，普特里克国家理工学院巴扬威angi分校）；Balai Besar Teknik Kesehatan Lingkungan dan P2B Surabaya（环境与P2B技术研究所Surabaya）
