Micro-CT images of the renal arteries of intact rat kidneys, which had their vasculature injected with the contrast agent polymer Microfil, were characterized. Measurement of inter-branch segment properties and the hierarchical structure of the vessel trees were computed by an automated algorithmic approach. The perfusion territories of the different kidneys, as well as the local diameters of the segmented vasculature were mapped onto the representative structures and visually explored. Various parameters were compared in order to outline key geometrical properties, properties which were shown to not have a wide range of inter-specimen variation. It is shown that the fractal scaling in non-symmetric branching reveals itself differently, than in symmetric branching (e.g., in the lung the mean bronchial diameters at each generation are closely related). Also, perfused tissue is shown to have very little inter-specimen variation and therefore could be used in future studies related to characterizing various disease states of tissues and organs based on vascular branching geometry.
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In a wide variety of fields, analysis of images involves defining a region and measuring its inherent properties. Such measurements include a region's surface area, curvature, volume, average gray and/or color scale, and so on. Furthermore, the subsequent subdivision of these regions is sometimes performed. These subdivisions are then used to measure local information, at even finer scales. However, simple griding or manual editing methods are typically used to subdivide a region into smaller units. The resulting subdivisions can therefore either not relate well to the actual shape or property of the region being studied (i.e., gridding methods), or be time consuming and based on user subjectivity (i.e., manual methods). The method discussed in this work extracts subdivisional units based on a region's general shape information. We present the results of applying our method to the medical image analysis of nested regions-of-interest of myocardial wall, where the subdivisions are used to study temporal and/or spatial heterogeneity of myocardial perfusion. This method is of particular interest for creating subdivision regions-of-interest (SROIs) when no variable intensity or other criteria within a region need be used to separate a particular region into subunits.
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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.
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The de facto standard of dynamic histogram binning for radiomic feature extraction leads to an elevated sensitivity to fluctuations in annotated regions. This may impact the majority of radiomic studies published recently and contribute to issues regarding poor reproducibility of radiomic-based machine learning that has led to significant efforts for data harmonization; however, we believe the issues highlighted here are comparatively neglected, but often remedied by choosing static binning. The field of radiomics has improved through the development of community standards and open-source libraries such as PyRadiomics. But differences in image acquisition, systematic differences between observers' annotations, and preprocessing steps still pose challenges. These can change the distribution of voxels altering extracted features and can be exacerbated with dynamic binning.
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最近一年带来了电动汽车(EV)和相关基础设施/通信的大幅进步。入侵检测系统(ID)被广泛部署在此类关键基础架构中的异常检测。本文提出了一个可解释的异常检测系统(RX-ADS),用于在电动汽车中的CAN协议中进行入侵检测。贡献包括:1)基于窗口的特征提取方法; 2)基于深度自动编码器的异常检测方法; 3)基于对抗机器学习的解释生成方法。在两个基准CAN数据集上测试了提出的方法:OTID和汽车黑客。将RX-ADS的异常检测性能与这些数据集的最新方法进行了比较:HID和GID。 RX-ADS方法提出的性能与HIDS方法(OTIDS数据集)相当,并且具有超出HID和GID方法(CAR HACKING DATASET)的表现。此外,所提出的方法能够为因各种侵入而引起的异常行为产生解释。这些解释后来通过域专家使用的信息来检测异常来验证。 RX-ADS的其他优点包括:1)该方法可以在未标记的数据上进行培训; 2)解释有助于专家理解异常和根课程分析,并有助于AI模型调试和诊断,最终改善了对AI系统的用户信任。
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光学相干断层扫描(OCT)是一种非侵入性技术,可在微米分辨率中捕获视网膜的横截面区域。它已被广泛用作辅助成像参考,以检测与眼睛有关的病理学并预测疾病特征的纵向进展。视网膜层分割是至关重要的特征提取技术之一,其中视网膜层厚度的变化和由于液体的存在而引起的视网膜层变形高度相关,与多种流行性眼部疾病(如糖尿病性视网膜病)和年龄相关的黄斑疾病高度相关。变性(AMD)。但是,这些图像是从具有不同强度分布或换句话说的不同设备中获取的,属于不同的成像域。本文提出了一种分割引导的域适应方法,以将来自多个设备的图像调整为单个图像域,其中可用的最先进的预训练模型可用。它避免了即将推出的新数据集的手动标签的时间消耗以及现有网络的重新培训。网络的语义一致性和全球特征一致性将最大程度地减少许多研究人员报告的幻觉效果,这些效应对周期矛盾的生成对抗网络(Cyclegan)体系结构。
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语言模型既展示了定量的改进,又展示了新的定性功能,随着规模的增加。尽管它们具有潜在的变革性影响,但这些新能力的特征却很差。为了为未来的研究提供信息,为破坏性的新模型能力做准备,并改善社会有害的效果,至关重要的是,我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战,我们介绍了超越模仿游戏基准(Big Bench)。 Big Bench目前由204个任务组成,由132家机构的442位作者贡献。任务主题是多样的,从语言学,儿童发展,数学,常识性推理,生物学,物理学,社会偏见,软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号,Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为,跨越了数百万到数十亿个参数。此外,一个人类专家评估者团队执行了所有任务,以提供强大的基准。研究结果包括:模型性能和校准都随规模改善,但绝对的术语(以及与评估者的性能相比);在模型类中的性能非常相似,尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分,而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标;社交偏见通常会随着含糊不清的环境而随着规模而增加,但这可以通过提示来改善。
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我们调查部分观察到的Markov决策过程(POMDPS),通过描述状态,观察和控制不确定性的熵术语规范化的成本函数。标准POMDP技术显示为对这些熵正则化的POMDP提供有界误差解决方案,当正规化涉及状态,观察和控制轨迹的联合熵时,具有精确的解决方案。我们的联合熵结果特别令人惊讶,因为它构成了一种新颖的,无解决的活性状态估计的制剂。
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目前,由精确的径向速度(RV)观察结果受到恒星活性引入的虚假RV信号的限制。我们表明,诸如线性回归和神经网络之类的机器学习技术可以有效地从RV观测中删除活动信号(由于星形/张图引起的)。先前的工作着重于使用高斯工艺回归等建模技术仔细地过滤活性信号(例如Haywood等人,2014年)。取而代之的是,我们仅使用对光谱线平均形状的更改进行系统地删除活动信号,也没有有关收集观测值的信息。我们对模拟数据(使用SOAP 2.0软件生成; Dumusque等人,2014年生成)和从Harps-N太阳能望远镜(Dumusque等,2015; Phillips等人2015; 2016; Collier训练)培训了机器学习模型。 Cameron等人2019)。我们发现,这些技术可以从模拟数据(将RV散射从82 cm/s提高到3 cm/s)以及从HARPS-N太阳能望远镜中几乎每天进行的600多种真实观察结果来预测和消除恒星活动(将RV散射从82 cm/s提高到3 cm/s)。 (将RV散射从1.753 m/s提高到1.039 m/s,提高了约1.7倍)。将来,这些或类似的技术可能会从太阳系以外的恒星观察中去除活动信号,并最终有助于检测到阳光状恒星周围可居住的区域质量系外行星。
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While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.
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