With the wide applications of colored point cloud in many fields, point cloud perceptual quality assessment plays a vital role in the visual communication systems owing to the existence of quality degradations introduced in various stages. However, the existing point cloud quality assessments ignore the mechanism of human visual system (HVS) which has an important impact on the accuracy of the perceptual quality assessment. In this paper, a progressive knowledge transfer based on human visual perception mechanism for perceptual quality assessment of point clouds (PKT-PCQA) is proposed. The PKT-PCQA merges local features from neighboring regions and global features extracted from graph spectrum. Taking into account the HVS properties, the spatial and channel attention mechanism is also considered in PKT-PCQA. Besides, inspired by the hierarchical perception system of human brains, PKT-PCQA adopts a progressive knowledge transfer to convert the coarse-grained quality classification knowledge to the fine-grained quality prediction task. Experiments on three large and independent point cloud assessment datasets show that the proposed no reference PKT-PCQA network achieves better of equivalent performance comparing with the state-of-the-art full reference quality assessment methods, outperforming the existed no reference quality assessment network.

Differentially private data generation techniques have become a promising solution to the data privacy challenge -- it enables sharing of data while complying with rigorous privacy guarantees, which is essential for scientific progress in sensitive domains. Unfortunately, restricted by the inherent complexity of modeling high-dimensional distributions, existing private generative models are struggling with the utility of synthetic samples. In contrast to existing works that aim at fitting the complete data distribution, we directly optimize for a small set of samples that are representative of the distribution under the supervision of discriminative information from downstream tasks, which is generally an easier task and more suitable for private training. Our work provides an alternative view for differentially private generation of high-dimensional data and introduces a simple yet effective method that greatly improves the sample utility of state-of-the-art approaches.

在医学中，图像注册对于图像引导的干预措施和其他临床应用至关重要。但是，很难解决，通过机器学习的出现，最近在该领域的医疗图像注册方面已经取得了很大的进步。深度神经网络的实施为某些医学应用提供了机会，例如在更少的时间内进行图像注册，以高精度，在操作过程中对抗肿瘤中发挥关键作用。当前的研究对基于无监督的深神经网络的医学图像注册研究的最新文献进行了全面的范围审查，其中包括到本领域在此日期中发表的所有相关研究。在这里，我们试图总结医学领域中无监督的基于深度学习的注册方法的最新发展和应用。在当前的全面范围审查中，精心讨论和传达了基本和主要概念，技术，从不同观点，新颖性和未来方向的统计分析。此外，这篇评论希望帮助那些被这一领域铆接的活跃读者深入了解这一激动人心的领域。

脉络膜丛（CP）是产生大部分脑脊液（CSF）的大脑的心室的结构。几个淘汰的课后和体内研究已经指出了它们在多发性硬化症（MS）中的炎症过程中的作用。因此，来自MRI的CP的自动分割具有高价值，用于研究其在大型患者的大队列中的特征。据我们所知，CP分段唯一可自由的工具是FreeSurfer，但其对该特定结构的准确性很差。在本文中，我们建议自动从非对比度增强的T1加权MRI自动分段。为此，我们介绍了一种基于轴向多层截图（MLP）的组件的“Axial-MLP”的新模型。这是最近的作品启发，表明，变压器的自我注意层可以用MLPS取代。系统地与标准的3D U-Net，NNU-Net，FreeSurfer和Fastsurefer系统地进行系统地进行系统地进行系统地进行。对于我们的实验，我们利用141个受试者的数据集（44个对照和97名MS患者）。我们展示所有测试的深度学习（DL）方法优于FreeSurfer（DIC为0.7的骰子，对于FreeSurfer的DL 0.33）。 Axial-MLP与U-Net竞争竞争，即使它略有略低于准确。我们纸张的结论是两倍：1）学习的深度学习方法可能是研究CP在MS患者的大型队列中的有用工具; 2）〜Axial-MLP是用于这种任务的卷积神经网络的潜在可行的替代方案，尽管它可以从进一步的改进中受益。