The availability of large-scale chest X-ray datasets is a requirement for developing well-performing deep learning-based algorithms in thoracic abnormality detection and classification. However, biometric identifiers in chest radiographs hinder the public sharing of such data for research purposes due to the risk of patient re-identification. To counteract this issue, synthetic data generation offers a solution for anonymizing medical images. This work employs a latent diffusion model to synthesize an anonymous chest X-ray dataset of high-quality class-conditional images. We propose a privacy-enhancing sampling strategy to ensure the non-transference of biometric information during the image generation process. The quality of the generated images and the feasibility of serving as exclusive training data are evaluated on a thoracic abnormality classification task. Compared to a real classifier, we achieve competitive results with a performance gap of only 3.5% in the area under the receiver operating characteristic curve.

出于研究目的，在发布大量此类数据集之前，胸部X光片的强大而可靠的匿名化构成了必不可少的步骤。传统的匿名过程是通过在图像中使用黑匣子中遮盖个人信息并删除或替换元信息来执行的。但是，这种简单的措施将生物识别信息保留在胸部X光片中，从而使患者可以通过连锁攻击重新识别。因此，我们看到迫切需要混淆图像中出现的生物特征识别信息。据我们所知，我们提出了第一种基于深度学习的方法，以目标匿名化胸部X光片，同时维护数据实用程序以诊断和机器学习目的。我们的模型架构是三个独立神经网络的组成，当共同使用时，它可以学习能够阻碍患者重新识别的变形场。通过消融研究研究每个组件的个体影响。 CHESTX-RAY14数据集的定量结果显示，在接收器操作特征曲线（AUC）下，患者重新识别从81.8％降低至58.6％，对异常分类性能的影响很小。这表明能够保留潜在的异常模式，同时增加患者隐私。此外，我们将提出的基于学习的深度匿名方法与差异化图像像素化进行比较，并证明了我们方法在解决胸部X光片的隐私性权衡权衡方面的优越性。

计算机断层摄影血管造影是一种关键的模态，提供了对脑血管血管树的见解，这对于对缺血性侦听的诊断和治疗至关重要，特别是在大容器闭塞（LVO）的情况下。因此，临床工作流程极大地受益于患有LVOS的患者的自动检测。这项工作采用卷积神经网络进行核心级分类，该仪表级分类培训，血管树分割掩模的弹性变形，以人为地增强训练数据。仅使用掩码作为我们模型的输入唯一允许我们在挡住样本现实时比一个传统图像体积更积极地施加这种变形。神经网络对LVO和受影响的半球的存在进行分类。在5倍交叉验证的消融研究中，我们证明了建议的增强的使用使我们即使从几个数据集中培训强大的模型。在100个数据集中培训高效网络架构，所提出的增强方案能够使用无增强的基线值将ROC AUC从0.57的基线值升高到0.85。使用3D-DENENET产生的AUC为0.88的3D DENSENET实现了最佳性能。增强对受影响半球的分类产生了积极影响，3D-DENENET在两侧达到0.93的AUC。

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.

This short paper discusses continually updated causal abstractions as a potential direction of future research. The key idea is to revise the existing level of causal abstraction to a different level of detail that is both consistent with the history of observed data and more effective in solving a given task.