Unsupervised anomaly detection in time-series has been extensively investigated in the literature. Notwithstanding the relevance of this topic in numerous application fields, a complete and extensive evaluation of recent state-of-the-art techniques is still missing. Few efforts have been made to compare existing unsupervised time-series anomaly detection methods rigorously. However, only standard performance metrics, namely precision, recall, and F1-score are usually considered. Essential aspects for assessing their practical relevance are therefore neglected. This paper proposes an original and in-depth evaluation study of recent unsupervised anomaly detection techniques in time-series. Instead of relying solely on standard performance metrics, additional yet informative metrics and protocols are taken into account. In particular, (1) more elaborate performance metrics specifically tailored for time-series are used; (2) the model size and the model stability are studied; (3) an analysis of the tested approaches with respect to the anomaly type is provided; and (4) a clear and unique protocol is followed for all experiments. Overall, this extensive analysis aims to assess the maturity of state-of-the-art time-series anomaly detection, give insights regarding their applicability under real-world setups and provide to the community a more complete evaluation protocol.

Using functional magnetic resonance imaging (fMRI) and deep learning to explore functional brain networks (FBNs) has attracted many researchers. However, most of these studies are still based on the temporal correlation between the sources and voxel signals, and lack of researches on the dynamics of brain function. Due to the widespread local correlations in the volumes, FBNs can be generated directly in the spatial domain in a self-supervised manner by using spatial-wise attention (SA), and the resulting FBNs has a higher spatial similarity with templates compared to the classical method. Therefore, we proposed a novel Spatial-Temporal Convolutional Attention (STCA) model to discover the dynamic FBNs by using the sliding windows. To validate the performance of the proposed method, we evaluate the approach on HCP-rest dataset. The results indicate that STCA can be used to discover FBNs in a dynamic way which provide a novel approach to better understand human brain.

与诊断放射学相关的患者护理质量与医师工作量成正比。分割是诊断和治疗程序的基本限制前体。机器学习的进步（ML）旨在提高诊断效率，以用广义算法替代单个应用程序。在无监督的异常检测（UAD）中，基于卷积神经网络（CNN）自动编码器（AES）和变异自动编码器（VAE）被视为基于重建的异常分段的事实方法。在医学图像中寻找异常区域是使用异常分割的主要应用之一。 CNN中受限制的接收场限制了CNN对全局上下文进行建模，因此，如果异常区域涵盖了图像的一部分，则基于CNN的AES无法带来对图像的语义理解。另一方面，视觉变压器（VIT）已成为CNN的竞争替代品。它依赖于能够将图像斑块相互关联的自我发挥机制。为了重建一个连贯和更现实的图像，在这项工作中，我们研究了变形金刚在为基于重建的UAD任务构建AES的功能中。我们专注于用于大脑磁共振成像（MRI）的异常分割，并呈现五个基于变压器的模型，同时可以使分割性能可比或与最新模型（SOTA）模型相当。源代码可在github https://github.com/ahmedgh970/transformers_unsupervise_anomaly_segentation.git上获得