Anomaly Detection and Characterisation with Few-Shot Machine Learning

使用少样本机器学习进行异常检测和表征

• 批准号: 2473191
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2473191
• 关键词: Anomaly; Detection; Characterisation; Few; Shot

Anomaly detection and image reconstruction are new and exciting frontiers of machine learning, employing the most sophisticated and efficient algorithms available in order to solve real and pressing issues. These types of solutions are incredibly active areas of research across a multitude of fields and subject areas, from detecting credit card fraud to reducing noise in captured images or video. In recent years, medical imaging has been a large sector looking to employ and tailor these methods. One of the most prominent of these specialist uses is in X-ray imaging. Post image reconstruction in this area has been shown to be sufficiently sound, allowing for reduction in scanning time and radioactive dose administered to patients, both of which are crucial in resource constrained settings or in cases of unstable or at risk individuals. When looking to use medical data in learning algorithms, obtaining labels is non trivial and requires scarce and expensive experts. For this reason unsupervised learning systems are much more likely to result in practical and useful implementations. This proposed project would look at researching and developing a novel unsupervised anomaly detection algorithm for use in the X-ray imaging sector where an anomaly could be fraudulent scans or atypical bone structure. Studies similar to this have been conducted on hand and chest images, both concluding with positive results. The project proposed here differs from these by use of image reconstruction techniques in aiding to detect these anomalies as well as the potential use of feature engineering, looking to model bone density and additional spatial information. There already exists multiple theoretical strategies in image reconstruction, all of which would be explored on a fundamental level in in order to develop a new algorithm suitable for use. Algorithms such as generative adversarial networks (GANs) and variational auto-encoders have been found to produce reasonable results and may provide an interesting starting point for research. The proposed feature engineering in this project is much more ambitious, however would ideally set the road for future development in this area. If possible, these additional features could be used in other aspects of X-ray imaging, such as density tagged data in scans where changes could be tracked over time, potentially aiding the diagnosis of conditions such as osteoarthritis and osteoporosis. The additional spatial information may include degree of bone separation, which could aid in the search for other bone related diseases. Feature extraction of bone density has been briefly explored using traditional machine learning methods and some deep convolutional neural networks (CNNs), however has not yet been extensively explored with other deep learning techniques or in conjunction with image reconstruction or anomaly detection. This project is intended to be ambitious from the start, where identifying how these areas of research may be brought together and utilised would be the primary focus. From here, it is likely that the project would narrow it's span and delve deeper into the development of novel deep learning algorithms in one of these discussed sub-areas.

异常检测和图像重建是机器学习的新领域，采用最复杂、最高效的算法来解决真实的紧迫问题。这些类型的解决方案是众多领域和主题领域中非常活跃的研究领域，从检测信用卡欺诈到减少捕获图像或视频中的噪声。近年来，医学成像一直是寻求采用和定制这些方法的一个大部门。这些专业用途中最突出的一个是X射线成像。已证明该区域中的图像后重建是充分合理的，允许减少扫描时间和给予患者的放射性剂量，这两者在资源受限的环境中或在不稳定或处于风险中的个体的情况下是至关重要的。当希望在学习算法中使用医疗数据时，获得标签并不简单，需要稀缺且昂贵的专家。出于这个原因，无监督学习系统更有可能导致实际和有用的实现。该拟议项目将研究和开发一种新型的无监督异常检测算法，用于X射线成像领域，其中异常可能是欺诈性扫描或非典型骨骼结构。类似的研究已经在手部和胸部图像上进行，两者都得出了积极的结果。这里提出的项目与这些不同，通过使用图像重建技术来帮助检测这些异常以及潜在的特征工程的使用，以模拟骨密度和其他空间信息。在图像重建中已经存在多种理论策略，所有这些都将在基础水平上进行探索，以开发适合使用的新算法。生成对抗网络（GAN）和变分自动编码器等算法已被发现产生合理的结果，并可能为研究提供一个有趣的起点。该项目中提出的功能工程更加雄心勃勃，但理想情况下将为该领域的未来发展铺平道路。如果可能的话，这些额外的功能可以用于X射线成像的其他方面，例如扫描中的密度标记数据，其中可以随时间跟踪变化，可能有助于诊断骨关节炎和骨质疏松症等疾病。附加的空间信息可以包括骨分离的程度，这可以帮助搜索其他骨相关疾病。已经使用传统的机器学习方法和一些深度卷积神经网络（CNN）对骨密度的特征提取进行了简要的探索，但是尚未使用其他深度学习技术或结合图像重建或异常检测进行广泛的探索。该项目从一开始就雄心勃勃，确定如何将这些研究领域结合在一起并加以利用将是主要重点。从这里开始，该项目可能会缩小其范围，并在这些讨论的子领域之一深入研究新型深度学习算法的开发。