脑小血管疾病的成像标记提供了有关脑部健康的宝贵信息，但是它们的手动评估既耗时又受到实质性内部和间际变异性的阻碍。自动化评级可能受益于生物医学研究以及临床评估，但是现有算法的诊断可靠性尚不清楚。在这里，我们介绍了\ textIt {血管病变检测和分割}（\ textit {v textit {where valdo？}）挑战，该挑战是在国际医学图像计算和计算机辅助干预措施（MICCAI）的卫星事件中运行的挑战（MICCAI） 2021.这一挑战旨在促进大脑小血管疾病的小而稀疏成像标记的自动检测和分割方法的开发，即周围空间扩大（EPVS）（任务1），脑微粒（任务2）和预先塑造的鞋类血管起源（任务3），同时利用弱和嘈杂的标签。总体而言，有12个团队参与了针对一个或多个任务的解决方案的挑战（任务1 -EPVS 4，任务2 -Microbleeds的9个，任务3 -lacunes的6个）。多方数据都用于培训和评估。结果表明，整个团队和跨任务的性能都有很大的差异，对于任务1- EPV和任务2-微型微型且对任务3 -lacunes尚无实际的结果，其结果尤其有望。它还强调了可能阻止个人级别使用的情况的性能不一致，同时仍证明在人群层面上有用。

深度学习模型显示了它们对多种应用的潜力。但是，大多数模型由于其复杂的推理而难以信任，通常被称为黑盒问题。一些领域，例如医学，需要高度透明度来接受和采用此类技术。因此，需要在分类器上创建可解释/可解释的模型或应用事后方法以在深度学习模型中建立信任。此外，深度学习方法可用于分割任务，这些任务通常需要难以实现的，耗时的手动淘汰分段标签进行培训。本文介绍了三个固有的可解释的分类器，以解决这两个问题。网络提供的本地化热图（代表模型的焦点区域并用于分类决策）可以直接解释，而无需任何事后方法来推导信息以进行模型说明。模型是通过使用输入图像的训练，仅以监督的方式将分类标签作为地面真相进行培训 - 无需使用有关感兴趣区域位置的任何信息（即细分标签），从而使模型的细分训练变得薄弱。 - 通过分类标签进行监管。最终的分割是通过阈值这些热图获得的。这些模型用于使用两个不同数据集进行多级脑肿瘤分类的任务，从而使监督分类任务的最佳F1得分为0.93，同时确保了弱点诉讼的0.67 $ \ pm 0.08 $ \ pm 0.08 $ \ pm。细分任务。此外，仅在肿瘤图像子集上获得的准确性优于最先进的神经胶质瘤肿瘤分级分类器，其最佳模型达到了98.7 \％的精度。

在临床环境中，通过视频脑电图（EEG）测试监测癫痫患者。视频EEG记录eEG设备记录其脑波时录像带的患者体验。目前，在癫痫发作期间，没有现有的自动化方法用于跟踪患者位置，以及医院患者的视频录制与公开的视频基准数据集大致不同。例如，摄像机角度可能是不寻常的，患者可以部分地覆盖有床上用品和电极组。能够与视频EEG实时跟踪患者将是提高医疗保健质量的有希望的创新。具体而言，自动患者检测系统可以补充临床监督，并降低需要连续监测患者的护士和医生的资源密集努力。我们评估了一个想象的预先训练的面罩R-CNN，一种标准的对象检测深度学习模型，用于使用我们自己的45岁患者45个视频的策划数据集的患者检测任务。数据集被聚合并策划此工作。我们展示没有微调的情况下，Imagenet预训练的掩模R-CNN模型在这些数据上表现不佳。通过微调具有我们数据集的子集的模型，我们观察患者检测性能的大量改善，平均平均精度为0.64。我们表明结果基本上取决于视频剪辑。

大脑的血管为人脑提供所需的营养和氧气。作为大脑血液供应的脆弱部分，小血管的病理可能会引起严重的问题，例如脑小血管疾病（CSVD）。还显示CSVD与神经变性有关，例如阿尔茨海默氏病。随着7个特斯拉MRI系统的发展，可以实现较高的空间图像分辨率，从而使大脑中非常小的血管描绘。非深度学习的方法进行血管分割的方法，例如，弗兰吉的血管增强，随后的阈值能够将培养基分割至大容器，但通常无法分割小血管。这些方法对小容器的敏感性可以通过广泛的参数调整或手动校正来提高，尽管使它们耗时，费力，并且对于较大的数据集而言是不可行的。本文提出了一个深度学习架构，以自动在7特斯拉3D飞行时间（TOF）磁共振血管造影（MRA）数据中自动分割小血管。该算法对仅11个受试者的小型半自动分段数据进行训练和评估；使用六个进行培训，两个进行验证，三个进行测试。基于U-NET多尺度监督的深度学习模型使用训练子集进行了训练，并以一种自我监督的方式使用变形 - 意识到的学习以改善概括性能。针对测试集对拟议的技术进行了定量和定性评估，并获得了80.44 $ \ pm $ 0.83的骰子得分。此外，将所提出的方法的结果与选定的手动分割区域（62.07结果骰子）进行了比较，并通过变形感知的学习显示出显着改善（18.98 \％）。

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.

Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere. This is particularly adapted for high-dimensional directional data such as texts. We propose in this article to estimate a von Mises mixture using a l 1 penalized likelihood. This leads to sparse prototypes that improve clustering interpretability. We introduce an expectation-maximisation (EM) algorithm for this estimation and explore the trade-off between the sparsity term and the likelihood one with a path following algorithm. The model's behaviour is studied on simulated data and, we show the advantages of the approach on real data benchmark. We also introduce a new data set on financial reports and exhibit the benefits of our method for exploratory analysis.

Passive monitoring of acoustic or radio sources has important applications in modern convenience, public safety, and surveillance. A key task in passive monitoring is multiobject tracking (MOT). This paper presents a Bayesian method for multisensor MOT for challenging tracking problems where the object states are high-dimensional, and the measurements follow a nonlinear model. Our method is developed in the framework of factor graphs and the sum-product algorithm (SPA). The multimodal probability density functions (pdfs) provided by the SPA are effectively represented by a Gaussian mixture model (GMM). To perform the operations of the SPA in high-dimensional spaces, we make use of Particle flow (PFL). Here, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance even in challenging multisensor MOT scenarios with single sensor measurements that have a lower dimension than the object positions. We perform a numerical evaluation in a passive acoustic monitoring scenario where multiple sources are tracked in 3-D from 1-D time-difference-of-arrival (TDOA) measurements provided by pairs of hydrophones. Our numerical results demonstrate favorable detection and estimation accuracy compared to state-of-the-art reference techniques.

Location-aware networks will introduce new services and applications for modern convenience, surveillance, and public safety. In this paper, we consider the problem of cooperative localization in a wireless network where the position of certain anchor nodes can be controlled. We introduce an active planning method that aims at moving the anchors such that the information gain of future measurements is maximized. In the control layer of the proposed method, control inputs are calculated by minimizing the traces of approximate inverse Bayesian Fisher information matrixes (FIMs). The estimation layer computes estimates of the agent states and provides Gaussian representations of marginal posteriors of agent positions to the control layer for approximate Bayesian FIM computations. Based on a cost function that accumulates Bayesian FIM contributions over a sliding window of discrete future timesteps, a receding horizon (RH) control is performed. Approximations that make it possible to solve the resulting tree-search problem efficiently are also discussed. A numerical case study demonstrates the intelligent behavior of a single controlled anchor in a 3-D scenario and the resulting significantly improved localization accuracy.

This paper presents an introduction to the state-of-the-art in anomaly and change-point detection. On the one hand, the main concepts needed to understand the vast scientific literature on those subjects are introduced. On the other, a selection of important surveys and books, as well as two selected active research topics in the field, are presented.

Our aim is to build autonomous agents that can solve tasks in environments like Minecraft. To do so, we used an imitation learning-based approach. We formulate our control problem as a search problem over a dataset of experts' demonstrations, where the agent copies actions from a similar demonstration trajectory of image-action pairs. We perform a proximity search over the BASALT MineRL-dataset in the latent representation of a Video PreTraining model. The agent copies the actions from the expert trajectory as long as the distance between the state representations of the agent and the selected expert trajectory from the dataset do not diverge. Then the proximity search is repeated. Our approach can effectively recover meaningful demonstration trajectories and show human-like behavior of an agent in the Minecraft environment.