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

胸部X线图像中胸部疾病的少镜头检测与识别

• 批准号: 2657660
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2657660
• 关键词: Few; shot; detection; recognition; thorax

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.

在医疗保健领域，一些病理可能很少见，因此训练集中的图像可能很少。此外，传统的有监督机器学习技术需要相当大的数据集，在临床环境中，由于它需要专业知识，因此获取这些数据集往往很费力。少镜头学习是机器学习的一个子领域，它意味着当我们有(几个班级)只有几个带有监督信息的训练样本时，我们的目标是从新的数据中学习。该项目将解决现有方法中的上述问题，以检测和识别胸部X光图像中的胸部疾病。该项目的目标包括：1)研究和实施在少镜头学习场景中将胸部疾病与胸部X光进行分类的新方法。2)研究并实施在少镜头学习场景中从胸部X光检测/定位胸部疾病的新方法。3)仔细开发以胸部X光为重点的少镜头学习方法的基准，以克服在少镜头学习场景中缺乏合适数据集的限制。4)考察基础数据集的选择对少镜头学习方法的影响。结果表明，基础数据集的选择对方法的精度等因素有很大影响。我们希望在我们开发的方法上验证和测量这种影响，并检查潜在的根本原因。5)建立一个有效的原型来检测和识别胸部X光图像中的胸部疾病。为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，并学习它们的主要思想/框架，以开发和实施我们针对胸部X光的方法。-对于目标3)，研究使用公开的胸部X光图像数据集开发基准的策略和指导方针。-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，并为实际应用开发使能技术。