Deep reinforcement learning for discovering and visualising biomarkers

该项目旨在开发新的方法，以发现和可视化从胸部计算机断层扫描图像的最佳生物标记，基于最近开发的深度强化学习技术的扩展。本项目中提出的扩展将通过允许以原始高分辨率对大维输入进行有效分析来促进医学图像分析。此外，该项目将是第一个能够发现与重要临床结果相关的以前未知的生物标记物的方法。该项目将在一个真实世界的案例研究数据集上验证该方法，该数据集涉及预测慢性疾病的五年存活期。

This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the first approach capable of discovering previously unknown biomarkers associated with important clinical outcomes. The project will validate the approach on a real-world case study data set concerning the prediction of five-year survival of chronic disease.