扩散张量成像(DTI)已被用于研究神经退行性疾病对神经途径的影响,这可能导致这些疾病的更可靠和早期诊断,以及更好地了解它们如何影响大脑。我们介绍了一种基于标记为DTI光纤数据和相应统计数据的智能视觉分析系统,用于研究患者组。系统的AI增强界面通过组织和整体分析空间引导用户,包括统计特征空间,物理空间和不同组的患者的空间。我们使用自定义机器学习管道来帮助缩小此大型分析空间,然后通过一系列链接可视化务实拨动它。我们使用来自Parkinson进展标记倡议的研究数据库的实际数据进行多种案例研究。
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Photo-identification (photo-id) is one of the main non-invasive capture-recapture methods utilised by marine researchers for monitoring cetacean (dolphin, whale, and porpoise) populations. This method has historically been performed manually resulting in high workload and cost due to the vast number of images collected. Recently automated aids have been developed to help speed-up photo-id, although they are often disjoint in their processing and do not utilise all available identifying information. Work presented in this paper aims to create a fully automatic photo-id aid capable of providing most likely matches based on all available information without the need for data pre-processing such as cropping. This is achieved through a pipeline of computer vision models and post-processing techniques aimed at detecting cetaceans in unedited field imagery before passing them downstream for individual level catalogue matching. The system is capable of handling previously uncatalogued individuals and flagging these for investigation thanks to catalogue similarity comparison. We evaluate the system against multiple real-life photo-id catalogues, achieving mAP@IOU[0.5] = 0.91, 0.96 for the task of dorsal fin detection on catalogues from Tanzania and the UK respectively and 83.1, 97.5% top-10 accuracy for the task of individual classification on catalogues from the UK and USA.
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可提供许多开源和商业恶意软件探测器。然而,这些工具的功效受到新的对抗性攻击的威胁,由此恶意软件试图使用例如机器学习技术来逃避检测。在这项工作中,我们设计了依赖于特征空间和问题空间操纵的对抗逃避攻击。它使用可扩展性导向特征选择来最大限度地通过识别影响检测的最关键的特征来最大限度地逃避。然后,我们将此攻击用作评估若干最先进的恶意软件探测器的基准。我们发现(i)最先进的恶意软件探测器容易受到简单的逃避策略,并且可以使用现成的技术轻松欺骗; (ii)特征空间操纵和问题空间混淆可以组合起来,以便在不需要对探测器的白色盒子理解的情况下实现逃避; (iii)我们可以使用解释性方法(例如,Shap)来指导特征操纵并解释攻击如何跨多个检测器传输。我们的调查结果阐明了当前恶意软件探测器的弱点,以及如何改善它们。
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