Classifying and localising future cancerous lesions

对未来的癌性病变进行分类和定位

• 批准号: 2895295
• 金额: --
• 依托单位: University of Surrey
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2895295
• 关键词: Classifying; localising; future; cancerous; lesions

For many types of cancer, early detection is one of the most powerful tools to improve treatment success. An effective early cancer detection mechanism can be achieved through population based health screening programs, where clinicians generally use different types of medical image data (obtained from asymptomatic patients) to detect cancerous lesions, such as masses or polyps. An important research question is if it is possible to improve even more treatment outcomes with the detection of early disease processes from medical images before lesions become visible. To enable the answering of this question, we propose the development of new and sophisticated time-series forecasting optimisation methods that take longitudinal medical image data to predict the probability that a patient will develop cancer and to show the most likely image regions where the cancerous lesions will be localised.

对于许多类型的癌症，早期发现是提高治疗成功率的最有力工具之一。有效的早期癌症检测机制可以通过基于人群的健康筛查计划来实现，其中临床医生通常使用不同类型的医学图像数据（从无症状患者获得）来检测癌性病变，例如肿块或息肉。一个重要的研究问题是，在病变变得可见之前，通过从医学图像中检测早期疾病过程，是否有可能改善更多的治疗结果。为了能够回答这个问题，我们提出了新的和复杂的时间序列预测优化方法的发展，采取纵向医学图像数据来预测的概率，病人将发展癌症，并显示最有可能的图像区域，其中癌病变将被定位。