Few-shot detection and recognition of thorax diseases in chest x-ray images

在医疗保健领域，一些病理可能是罕见的，因此训练集中的图像可能是稀缺的。此外，传统的监督式机器学习技术需要大量的数据集，而在临床环境中，由于需要专业知识，通常很难获得这些数据集。few -shot学习是机器学习的一个子领域，它意味着我们的目标是从新数据中学习，当我们只有（几个类）几个带有监督信息的训练样本时。本项目将解决现有胸部x线图像中胸部疾病检测和识别方法中的上述问题。本项目的目标包括：1)研究和实现在少镜头学习场景下从胸部x射线中分类胸部疾病的新方法。2)研究和实施在几次学习场景下通过胸部x射线检测/定位胸部疾病的新方法。3)认真开发以胸部x射线为重点的少镜头学习方法的基准，这将克服少镜头学习场景缺乏适当数据集的限制。4)研究基础数据集选择对少镜头学习方法的影响。研究表明，基础数据集的选择对方法的精度等因素有很大影响。我们想要验证和测量对我们开发的方法的影响，以及检查潜在的根本原因。5)建立胸x线图像中胸部疾病的少镜头检测与识别的有效原型。为了达到相应的目标，将采取的方法是：-对于目标1)和2)，研究现有的少镜头学习方法，并从他们的主要思想/框架中学习，以开发和实施我们针对胸部x射线的方法。-对于目标3)，研究使用公开可用的胸部x线图像数据集制定基准的策略和指南。-对于4)和5)：使用我们自己和第三方的方法来检查基础数据集选择的效果，并为实际应用开发启用技术。

In the healthcare domain, some pathologies may be rare and therefore images in the training set may be scarce. Furthermore, traditional supervised machine learning techniques require significantly large datasets which, in a clinical setting, is often laborious to obtain as it necessitates specialist knowledge. Few-shot learning is a sub-area of machine learning and implies that we aim to learn from new data when we have (several classes with) only a few training samples with supervised information. This project is going to address the aforementioned issues in the existing approaches to detection and recognition of thorax diseases in chest x-ray images. The objectives of this project include:1) To investigate and implement novel methods to classify thorax diseases from chest x-rays in a few-shot learning scenario.2) To investigate and implement novel methods to detect/localise thorax diseases from chest x-rays in a few-shot learning scenario.3) To carefully develop a benchmark for few-shot learning methods which focus on chest x-rays, which is going to overcome the limitation in a lack of appropriate datasets for few-shot learning scenarios. 4) To investigate the effects and impact of base-dataset selection on few-shot learning methods. It has been shown that the selection of the base-dataset considerably influences elements such as the accuracy of the method. We want to verify and measure this effect on our developed methods, as well as examine potential root causes. 5)To establish an effective prototype for few-shot detection and recognition of thorax diseases in chest x-ray images.The approaches that will be taken to meet the corresponding objectives are:- For objectives 1) and 2), study the existing few-shot learning methods and learn from their main ideas/frameworks to develop and implement our method which targets chest x-rays.- For objective 3), examine strategies and guidelines to develop a benchmark using publicly available chest x-ray image datasets.- For 4) and 5): Use both our own and third-party methods to inspect the effects of base-dataset selection and develop enabling techniques for real applications.