具有大脑般的组织和设备物理学的混合信号神经形态处理器为传统深度学习和计算的不可持续发展提供了超低功率的替代方案。但是，意识到这种神经形态硬件的潜力需要有效利用其异质的，模拟神经突触电路，采用神经计算方法来稀疏，基于尖峰的编码和处理。在这里，我们研究了平衡兴奋性抑制性抑制性横向连接作为实施丘脑皮层启发的时空相关器（STC）神经网络的一种资源有效机制，而无需使用专用的延迟机制。我们提出了使用DynAP-SE神经形态处理器进行硬件的环境实验，其中在STC网络中，在STC网络中，无均匀重合检测神经元的接收场通过随机输入采样绘制，每个列中有四个侧向传入连接。此外，我们演示了如何调整这种神经元来检测特定的时空特征，该特征通过模拟突触电路的离散地址编程。双突触连接的能量耗散是每个横向连接（0.65 NJ vs 9.6 NJ）比STC的前一个基于延迟的硬件实现的数量级（0.65 nj vs 9.6 NJ）。

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.

Workplace injuries are common in today's society due to a lack of adequately worn safety equipment. A system that only admits appropriately equipped personnel can be created to improve working conditions. The goal is thus to develop a system that will improve workers' safety using a camera that will detect the usage of Personal Protective Equipment (PPE). To this end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system into an entry control point where workers must present themselves to obtain access to a restricted area. Combined with facial identity recognition, the system would ensure that only authorized people wearing appropriate equipment are granted access. A novelty of this work is that we increase the number of classes to five objects (hardhat, safety vest, safety gloves, safety glasses, and hearing protection), whereas most existing works only focus on one or two classes, usually hardhats or vests. The AI model developed provides good detection accuracy at a distance of 3 and 5 meters in the collaborative environment where we aim at operating (mAP of 99/89%, respectively). The small size of some objects or the potential occlusion by body parts have been identified as potential factors that are detrimental to accuracy, which we have counteracted via data augmentation and cropping of the body before applying PPE detection.

One of the biggest challenges in machine learning is data collection. Training data is an important part since it determines how the model will behave. In object classification, capturing a large number of images per object and in different conditions is not always possible and can be very time-consuming and tedious. Accordingly, this work explores the creation of artificial images using a game engine to cope with limited data in the training dataset. We combine real and synthetic data to train the object classification engine, a strategy that has shown to be beneficial to increase confidence in the decisions made by the classifier, which is often critical in industrial setups. To combine real and synthetic data, we first train the classifier on a massive amount of synthetic data, and then we fine-tune it on real images. Another important result is that the amount of real images needed for fine-tuning is not very high, reaching top accuracy with just 12 or 24 images per class. This substantially reduces the requirements of capturing a great amount of real data.

Fires have destructive power when they break out and affect their surroundings on a devastatingly large scale. The best way to minimize their damage is to detect the fire as quickly as possible before it has a chance to grow. Accordingly, this work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. Object detection has made giant leaps in speed and accuracy over the last six years, making real-time detection feasible. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system in an industrial warehouse setting, which is characterized by high ceilings. A drawback of traditional smoke detectors in this setup is that the smoke has to rise to a sufficient height. The AI models brought forward in this research managed to outperform these detectors by a significant amount of time, providing precious anticipation that could help to minimize the effects of fires further.

常规进行了视频支气管镜检查，以涉嫌癌症，监测COPD患者的肺组织活检以及在重症监护病房中澄清急性呼吸问题。复杂的支气管树中的导航尤其具有挑战性和身体要求，需要医生的长期经验。本文介绍了支气管镜视频中支气管孔的自动分割。由于缺乏易于获取的地面真相分段数据，目前，基于学习的深度方法被阻碍。因此，我们提出了一个由K均值组成的数据驱动管道，然后是基于紧凑的标记的流域算法，该算法能够从给定的深度图像中生成气道实例分割图。通过这种方式，这些传统算法是仅基于Phantom数据集的RGB图像上直接在RGB图像上训练浅CNN的弱监督。我们在两个体内数据集上评估了该模型的概括能力，这些数据集涵盖21个不同的支气管镜上的250帧。我们证明其性能与那些在体内数据中直接训练的模型相当，通过128x128的图像分辨率，对于检测到的气道分割中心的平均误差为11 vs 5像素。我们的定量和定性结果表明，在视频支气管镜检查，幻影数据和弱监督的背景下，使用基于非学习的方法可以获得对气道结构的语义理解。

为了能够在不怀疑的情况下使用人工智能（AI）在医学中，并认识到和评估其日益增长的潜力，在当前和未来的医务人员中，对该主题的基本理解是必要的。在“通过理解的信任”的前提下，我们在德国Ki校园（AI校园）项目框架内开发了创新的在线课程，这是一个自我指导的课程，它教授AI的基础知识进行分析医疗图像数据。主要目标是提供一个学习环境，以充分了解医学图像分析中的AI，以便通过积极的应用经验来克服对该主题的进一步兴趣，并可以克服对其使用的抑制。重点是医疗应用和机器学习的基础。在线课程分为连续的课程，其中包括以解释性视频的形式，以简化和实践练习和/或测验的形式进行的实践练习，以检查学习进度。在课程的第一次跑步中，参与医学生的一项调查用于定量分析我们的研究假设。

最近基于深度学习的医学图像注册方法实现了与传统优化算法在减少的运行时间时具有竞争力的结果。但是，深度神经网络通常需要大量标记的培训数据，并且容易受到培训和测试数据之间的领域变化。尽管基于按键的注册可以减轻典型的强度移位，但由于不同的视野，这些方法仍然遭受几何域移位。作为一种补救措施，在这项工作中，我们提出了一种用于图像注册的几何结构域适应性的新方法，将模型从标记的源调整为未标记的目标域。我们以基于按键的注册模型为基础，将用于几何特征学习的图形卷积与循环信念优化相结合，并提议通过自我增压来减少域的转移。为此，我们将模型嵌入了卑鄙的教师范式中。我们将平均教师扩展到这种情况下，通过1）调整随机增强方案和2）将学习的特征提取与可区分优化相结合。这使我们能够通过对学习学生和时间平均的教师模型的一致预测来指导未标记的目标域中的学习过程。我们评估了在两个具有挑战性的适应方案（dir-lab 4d ct to copd，copd to copd to Learn2Reg）下呼气到肺CT注册的方法。我们的方法一致地将基线模型提高了50％/47％，甚至匹配了对目标数据训练的模型的准确性。源代码可在https://github.com/multimodallearning/registration-da-mean-teacher上获得。

在连续的minimax游戏中找到平衡点已成为机器学习中的关键问题，部分原因是它与生成对抗网络的培训有关。由于存在和鲁棒性问题，最近的发展已从纯平的平衡转变为重点放在混合平衡点上。在本说明中，我们考虑了Domingo-Enrich等人提出的一种方法。在两层零和游戏中找到混合平衡。该方法基于熵正则化，两种竞争策略由两组相互作用的粒子表示。我们表明，随着粒子的数量增长到无穷大，粒子系统的经验度量序列满足了一个较大的偏差原理，以及这意味着经验度量的收敛性和相关的nikaid \^o-Isoda错误，以补充现有的定律大量结果。

为了实现安全的自动驾驶汽车（AV）操作，至关重要的是，AV的障碍检测模块可以可靠地检测出构成安全威胁的障碍物（即是安全至关重要的）。因此，希望对感知系统的评估指标捕获对象的安全性 - 临界性。不幸的是，现有的感知评估指标倾向于对物体做出强烈的假设，而忽略了代理之间的动态相互作用，因此不能准确地捕获现实中的安全风险。为了解决这些缺点，我们通过考虑自我车辆和现场障碍之间的闭环动态相互作用来引入互动障碍感知障碍检测评估度量指标。通过从最佳控制理论借用现有理论，即汉密尔顿 - 雅各比的可达性，我们提出了一种可构造``安全区域''的计算障碍方法：一个国家空间中的一个区域，该区域定义了安全 - 关键障碍为了定义安全目的的位置指标。我们提出的安全区已在数学上完成，并且可以轻松计算以反映各种安全要求。使用Nuscenes检测挑战排行榜的现成检测算法，我们证明我们的方法是计算轻量级，并且可以更好地捕获与基线方法更好地捕获关键的安全感知错误。