由于它们所需的大量集中，最深度增强学习算法的状态是对渐近性能的大量集中的效率低。由哺乳动物海马的启发的episodic加强学习（ERL）算法通常使用扩展的内存系统从过去的事件开始学习，以克服这个样本效率问题。然而，这种内存增强通常用作仅仅是缓冲区，从中绘制了孤立的过去经验，以便以离线方式学习（例如，重播）。这里，我们证明包括从集扩展抽样顺序导出的所获取的内存内容中的偏差来提高弹性控制算法的样本和存储器效率。我们在觅食任务中测试了我们的顺序焦点控制（SEC）模型，以显示存储和使用集成剧集作为事件序列导致更快的学习，与较少的内存要求相反，与隔离的缓冲区相比只有事件。我们还研究了内存约束的影响，忘记了SEC算法的顺序和非顺序版本。此外，我们讨论了类似海马的快速记忆系统如何在哺乳动物大脑中引导慢速皮质和皮质学习习惯的习惯。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。

The vulnerabilities of fingerprint-based recognition systems to direct attacks with and without the cooperation of the user are studied. Two different systems, one minutiae-based and one ridge feature-based, are evaluated on a database of real and fake fingerprints. Based on the fingerprint images quality and on the results achieved on different operational scenarios, we obtain a number of statistically significant observations regarding the robustness of the systems.

Reliable application of machine learning-based decision systems in the wild is one of the major challenges currently investigated by the field. A large portion of established approaches aims to detect erroneous predictions by means of assigning confidence scores. This confidence may be obtained by either quantifying the model's predictive uncertainty, learning explicit scoring functions, or assessing whether the input is in line with the training distribution. Curiously, while these approaches all state to address the same eventual goal of detecting failures of a classifier upon real-life application, they currently constitute largely separated research fields with individual evaluation protocols, which either exclude a substantial part of relevant methods or ignore large parts of relevant failure sources. In this work, we systematically reveal current pitfalls caused by these inconsistencies and derive requirements for a holistic and realistic evaluation of failure detection. To demonstrate the relevance of this unified perspective, we present a large-scale empirical study for the first time enabling benchmarking confidence scoring functions w.r.t all relevant methods and failure sources. The revelation of a simple softmax response baseline as the overall best performing method underlines the drastic shortcomings of current evaluation in the abundance of publicized research on confidence scoring. Code and trained models are at https://github.com/IML-DKFZ/fd-shifts.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

为了识别动态网络中嵌入的系统（模块），必须制定一个多输入估计问题，该问题需要测量某些节点并将其作为预测输入。但是，由于传感器选择和放置问题，在许多实际情况下，其中一些节点可能无法测量。这可能会导致目标模块的偏差估计。此外，与多输入结构相关的识别问题可能需要确定实验者不特别感兴趣的大量参数，并且在大型网络中的计算复杂性增加。在本文中，我们通过使用数据增强策略来解决这些问题，该策略使我们能够重建缺失的节点测量并提高估计目标模块的准确性。为此，我们使用基于正规化的基于内核的方法和近似推理方法开发了系统识别方法。为感兴趣的模块保留一个参数模型，我们将其他模块作为高斯过程（GP）建模，并用所谓的稳定样条核给出的内核。经验贝叶斯（EB）方法用于估计目标模块的参数。相关的优化问题是使用预期最大化（EM）方法来解决的，在该方法中，我们采用马尔可夫链蒙特卡洛（MCMC）技术来重建未知的缺失节点信息和网络动力学。动态网络示例上的数值模拟说明了开发方法的电势。

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。

为了利用同一场景的视频框架中的高时间相关性，使用基于块的运动估计和补偿技术从已经编码的参考帧中预测了当前帧。尽管这种方法可以有效利用移动对象的翻译运动，但它容易受到其他类型的仿射运动和对象遮挡/除含量的影响。最近，深度学习已被用来模拟人类姿势的高级结构，以从短视频中的特定动作中进行，然后通过使用生成的对抗网络（GAN）来预测姿势，从而在未来的时间内生成虚拟框架。因此，建模人姿势的高级结构能够通过预测人类的行为并确定其轨迹来利用语义相关性。视频监视应用程序将受益，因为可以通过估算人类姿势轨迹并通过语义相关性产生未来的框架来压缩存储的大监视数据。本文通过从已经编码的框架中对人姿势进行建模并在当前时间使用生成的框架来探讨一种新的视频编码方式。预计所提出的方法可以通过预测包含具有较低残差的移动对象的块来克服传统向后引用框架的局限性。实验结果表明，提出的方法平均可以实现高达2.83 dB PSNR增益和25.93 \％比特率的节省，用于高运动视频序列

骨肉瘤是最常见的原发性骨癌，其标准治疗包括术前化疗，然后切除。化学疗法反应用于预测患者的预后和进一步治疗。坏死在切除标本上的组织学幻灯片通常评估了坏死比定义为坏死肿瘤与总体肿瘤之比。已知坏死比> = 90％的患者的预后更好。多个载玻片对坏死比的手动微观综述是半定量性的，并且可能具有观察者间和观察者间的变异性。我们提出了一种基于目标和可再现的深度学习方法，以估计坏死比，并从扫描的苏木精和曙红全幻灯片图像预测结果。我们以3134个WSI的速度收集了103例骨肉瘤病例，以训练我们的深度学习模型，验证坏死比评估并评估结果预测。我们训练了深层多磁化网络，以分割多个组织亚型，包括生存的肿瘤和像素级中的坏死肿瘤，并计算来自多个WSI的病例级坏死比。我们显示了通过分割模型估算的坏死比，高度与由专家手动评估的病理报告中的坏死比高度相关，其中IV级的平均绝对差异（100％），III（> = 90％）和II（> = 50％和<50％和< 90％）坏死反应分别为4.4％，4.5％和17.8％。我们成功地对患者进行了分层，以预测P = 10^-6的总生存率，而P = 0.012的无进展生存率。我们没有可变性的可重现方法使我们能够调整截止阈值，特别是用于模型和数据集的截止阈值，为OS的80％，PFS为60％。我们的研究表明，深度学习可以支持病理学家作为一种客观的工具，可以分析组织学中骨肉瘤，以评估治疗反应并预测患者结果。