病变分割是放射线工作流程的关键步骤。手动分割需要长时间的执行时间,并且容易发生可变性,从而损害了放射线研究及其鲁棒性的实现。在这项研究中,对非小细胞肺癌患者的计算机断层扫描图像进行了深入学习的自动分割方法。还评估了手动与自动分割在生存放射模型的性能中的使用。方法总共包括899名NSCLC患者(2个专有:A和B,1个公共数据集:C)。肺部病变的自动分割是通过训练先前开发的建筑NNU-NET进行的,包括2D,3D和级联方法。用骰子系数评估自动分割的质量,以手动轮廓为参考。通过从数据集A的手动和自动轮廓中提取放射性的手工制作和深度学习特征来探索自动分割对患者生存的放射素模型对患者生存的性能的影响。评估并比较模型的精度。结果通过平均2D和3D模型的预测以及应用后处理技术来提取最大连接的组件,可以实现具有骰子= 0.78 +(0.12)的自动和手动轮廓之间的最佳一致性。当使用手动或自动轮廓,手工制作或深度特征时,在生存模型的表现中未观察到统计差异。最好的分类器显示出0.65至0.78之间的精度。结论NNU-NET在自动分割肺部病变中的有希望的作用已得到证实,从而大大降低了时必的医生的工作量,而不会损害基于放射线学的生存预测模型的准确性。
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Crop type maps are critical for tracking agricultural land use and estimating crop production. Remote sensing has proven an efficient and reliable tool for creating these maps in regions with abundant ground labels for model training, yet these labels remain difficult to obtain in many regions and years. NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar instrument, originally designed for forest monitoring, has shown promise for distinguishing tall and short crops. In the current study, we leverage GEDI to develop wall-to-wall maps of short vs tall crops on a global scale at 10 m resolution for 2019-2021. Specifically, we show that (1) GEDI returns can reliably be classified into tall and short crops after removing shots with extreme view angles or topographic slope, (2) the frequency of tall crops over time can be used to identify months when tall crops are at their peak height, and (3) GEDI shots in these months can then be used to train random forest models that use Sentinel-2 time series to accurately predict short vs. tall crops. Independent reference data from around the world are then used to evaluate these GEDI-S2 maps. We find that GEDI-S2 performed nearly as well as models trained on thousands of local reference training points, with accuracies of at least 87% and often above 90% throughout the Americas, Europe, and East Asia. Systematic underestimation of tall crop area was observed in regions where crops frequently exhibit low biomass, namely Africa and South Asia, and further work is needed in these systems. Although the GEDI-S2 approach only differentiates tall from short crops, in many landscapes this distinction goes a long way toward mapping the main individual crop types. The combination of GEDI and Sentinel-2 thus presents a very promising path towards global crop mapping with minimal reliance on ground data.
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Synthetic data generation has recently gained widespread attention as a more reliable alternative to traditional data anonymization. The involved methods are originally developed for image synthesis. Hence, their application to the typically tabular and relational datasets from healthcare, finance and other industries is non-trivial. While substantial research has been devoted to the generation of realistic tabular datasets, the study of synthetic relational databases is still in its infancy. In this paper, we combine the variational autoencoder framework with graph neural networks to generate realistic synthetic relational databases. We then apply the obtained method to two publicly available databases in computational experiments. The results indicate that real databases' structures are accurately preserved in the resulting synthetic datasets, even for large datasets with advanced data types.
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Explainable artificial intelligence (XAI) provides explanations for not interpretable machine learning (ML) models. While many technical approaches exist, there is a lack of validation of these techniques on real-world datasets. In this work, we present a use-case of XAI: an ML model which is trained to estimate electrification rates based on mobile phone data in Senegal. The data originate from the Data for Development challenge by Orange in 2014/15. We apply two model-agnostic, local explanation techniques and find that while the model can be verified, it is biased with respect to the population density. We conclude our paper by pointing to the two main challenges we encountered during our work: data processing and model design that might be restricted by currently available XAI methods, and the importance of domain knowledge to interpret explanations.
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Specular microscopy assessment of the human corneal endothelium (CE) in Fuchs' dystrophy is challenging due to the presence of dark image regions called guttae. This paper proposes a UNet-based segmentation approach that requires minimal post-processing and achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dystrophy. We cast the segmentation problem as a regression task of the cell and gutta signed distance maps instead of a pixel-level classification task as typically done with UNets. Compared to the conventional UNet classification approach, the distance-map regression approach converges faster in clinically relevant parameters. It also produces morphometric parameters that agree with the manually-segmented ground-truth data, namely the average cell density difference of -41.9 cells/mm2 (95% confidence interval (CI) [-306.2, 222.5]) and the average difference of mean cell area of 14.8 um2 (95% CI [-41.9, 71.5]). These results suggest a promising alternative for CE assessment.
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本文介绍了针对自闭症儿童的社会辅助机器人技术,旨在使用机器人疗法进行自闭症。该项目的目的是测试自闭症儿童与社会机器人NAO的互动。特别是机器人将在其工作中支持操作员(心理学家,教育者,言语治疗师等)。该项目的创新方面是,儿童机器人互动将考虑孩子的情绪和特定特征,机器人将相应地适应其行为。
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自引入以来,图形注意力网络在图表表示任务中取得了出色的结果。但是,这些网络仅考虑节点之间的成对关系,然后它们无法完全利用许多现实世界数据集中存在的高阶交互。在本文中,我们介绍了细胞注意网络(CANS),这是一种在图表上定义的数据上运行的神经体系结构,将图表示为介绍的细胞复合物的1个骨骼,以捕获高阶相互作用。特别是,我们利用细胞复合物中的下层和上层社区来设计两种独立的掩盖自我发项机制,从而推广了常规的图形注意力策略。罐中使用的方法是层次结构的,并结合了以下步骤:i)从{\ it node demantion}中学习{\ it Edge功能}的提升算法}; ii)一种细胞注意机制,可以在下层和上邻居上找到边缘特征的最佳组合; iii)层次{\ it Edge Pooling}机制,以提取一组紧凑的有意义的功能集。实验结果表明,CAN是一种低复杂性策略,它与基于图的学​​习任务的最新结果相比。
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我们提出了一种几何多级优化方法,该方法平滑地包含了框约束。给定一个受限的优化问题,我们考虑了具有不同离散水平的模型的层次结构。更精细的型号准确但计算昂贵,而更粗的型号则不太准确,但计算便宜。在良好级别上工作时,多级优化将基于搜索方向计算搜索方向,该模型会加快良好级别的更新。此外,利用层次结构引起的几何形状保留了更新的可行性。特别是,我们的方法扩展了多移民方法的经典组成部分,例如限制和延长延长我们约束的riemannian结构。
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该报告说明了基于音频和视频数据的最成功的AAL应用程序和功能的艺术状态,即(i)生命式和自我监控,(ii)对生命体征的远程监控,(iii)情绪状态识别,((iv)食物摄入量监测,活动和行为认识,(v)活动和个人帮助,(vi)手势识别,(vii)秋季检测和预防,(viii)移动性评估和脆弱的识别以及(IX)认知和运动康复。对于这些应用程序方案,该报告说明了科学进步,可用产品和研究项目的状态。开放的挑战也被突出显示。
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以知情方式监测和管理地球林是解决生物多样性损失和气候变化等挑战的重要要求。虽然森林评估的传统或空中运动提供了在区域一级分析的准确数据,但将其扩展到整个国家,以外的高度分辨率几乎不可能。在这项工作中,我们提出了一种贝叶斯深度学习方法,以10米的分辨率为全国范围的森林结构变量,使用自由可用的卫星图像作为输入。我们的方法将Sentinel-2光学图像和Sentinel-1合成孔径雷达图像共同变换为五种不同的森林结构变量的地图:95th高度百分位,平均高度,密度,基尼系数和分数盖。我们从挪威的41个机载激光扫描任务中培训和测试我们的模型,并证明它能够概括取消测试区域,从而达到11%和15%之间的归一化平均值误差,具体取决于变量。我们的工作也是第一个提出贝叶斯深度学习方法的工作,以预测具有良好校准的不确定性估计的森林结构变量。这些提高了模型的可信度及其适用于需要可靠的信心估计的下游任务,例如知情决策。我们提出了一组广泛的实验,以验证预测地图的准确性以及预测的不确定性的质量。为了展示可扩展性,我们为五个森林结构变量提供挪威地图。
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