Classifying and localising future cancerous lesions

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项目来实现，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）来检测癌症病变，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，是否有可能通过医学图像检测早期疾病过程来改善更多的治疗结果。为了回答这个问题，我们建议开发新的复杂的时间序列预测优化方法，该方法采用纵向医学图像数据来预测患者患癌症的概率，并显示最可能的图像区域，其中癌症病变将被定位。

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.