Anomaly Detection and Characterisation with Few-Shot Machine Learning

异常检测和图像重建是机器学习的新的和令人兴奋的前沿，它们使用最复杂和最有效的算法来解决现实和紧迫的问题。这些类型的解决方案在众多领域和主题领域都是非常活跃的研究领域，从检测信用卡欺诈到降低捕获图像或视频中的噪音。近年来，医学成像一直是寻求采用和定制这些方法的一个很大的部门。其中最突出的专业用途之一是X射线成像。这一领域的图像后重建已被证明是足够健全的，可以减少扫描时间和对患者进行的辐射剂量，这两者在资源有限的情况下或在不稳定或有风险的个人的情况下都是至关重要的。当希望在学习算法中使用医疗数据时，获得标签并不是一件容易的事情，需要稀缺而昂贵的专家。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。这个拟议的项目将着眼于研究和开发一种新的无监督异常检测算法，用于X射线成像部门，其中异常可能是欺诈性扫描或不典型的骨结构。类似的研究已经在手部和胸部图像上进行，结果都是积极的。这里提出的项目与这些项目不同，它使用图像重建技术来帮助检测这些异常，并潜在地使用特征工程来模拟骨密度和额外的空间信息。在图像重建中已经有了多种理论策略，所有这些都将在基础水平上进行探索，以开发出一种适合使用的新算法。生成性对抗网络(GANS)和变分自动编码器等算法已经被发现能够产生合理的结果，并可能为研究提供一个有趣的起点。这个项目中拟议的功能工程要雄心勃勃得多，但理想的情况是为这一领域的未来发展设定道路。如果可能，这些附加功能可以用于X射线成像的其他方面，例如扫描中的密度标记数据，可以跟踪随着时间的变化，潜在地帮助诊断骨关节炎和骨质疏松症等情况。额外的空间信息可以包括骨分离的程度，这可能有助于搜索其他骨相关疾病。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，但还没有被广泛地应用于其他深度学习技术，或与图像重建或异常检测相结合。这个项目从一开始就打算雄心勃勃，其中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的重点。从这里开始，该项目很可能会缩小它的范围，并在这些讨论的子领域之一更深入地研究新的深度学习算法的开发。

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.