Anomaly detection (AD) is a fundamental research problem in machine learning
and computer vision, with practical applications in industrial inspection,
video surveillance, and medical diagnosis. In medical imaging, AD is especially
vital for detecting and diagnosing anomalies that may indicate rare diseases or
conditions. However, there is a lack of a universal and fair benchmark for
evaluating AD methods on medical images, which hinders the development of more
generalized and robust AD methods in this specific domain. To bridge this gap,
we introduce a comprehensive evaluation benchmark for assessing anomaly
detection methods on medical images. This benchmark encompasses six reorganized
datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT,
chest X-ray, and digital histopathology) and three key evaluation metrics, and
includes a total of fourteen state-of-the-art AD algorithms. This standardized
and well-curated medical benchmark with the well-structured codebase enables
comprehensive comparisons among recently proposed anomaly detection methods. It
will facilitate the community to conduct a fair comparison and advance the
field of AD on medical imaging. More information on BMAD is available in our
GitHub repository: this https URL

本次研究针对于医学图像领域中，异常检测方法的广泛应用，建立了包含六个重新组织的数据集、三种关键指标和 14 种业内领先的 AD 算法的全面评估基准。该基准将有利于这个领域内进行公正的比较与优化，共同推进医学图像领域中异常检测算法的发展。

BMAD: 医疗异常检测基准

BMAD: Benchmarks for Medical Anomaly Detection

Unsupervised anomaly detection (AD) is critical for a wide range of practical
applications, from network security to health and medical tools. Due to the
diversity of problems, no single algorithm has been found to be superior for
all AD tasks. Choosing an algorithm, otherwise known as the Algorithm Selection
Problem (ASP), has been extensively examined in supervised classification
problems, through the use of meta-learning and AutoML, however, it has received
little attention in unsupervised AD tasks. This research proposes a new
meta-learning approach that identifies an appropriate unsupervised AD algorithm
given a set of meta-features generated from the unlabelled input dataset. The
performance of the proposed meta-learner is superior to the current state of
the art solution. In addition, a mixed model statistical analysis has been
conducted to examine the impact of the meta-learner components: the meta-model,
meta-features, and the base set of AD algorithms, on the overall performance of
the meta-learner. The analysis was conducted using more than 10,000 datasets,
which is significantly larger than previous studies. Results indicate that a
relatively small number of meta-features can be used to identify an appropriate
AD algorithm, but the choice of a meta-model in the meta-learner has a
considerable impact.

该研究提出了一种新的元学习方法，通过使用从未标记的输入数据集生成的元特征，识别出适当的无监督异常检测算法。该方法的性能优于现有的解决方案，并使用超过 10000 个数据集进行了混合模型统计分析，结果表明相对较少的元特征就足以识别合适的无监督异常检测算法，但在元学习器中选择元模型具有相当大的影响。