Learning MRI and histology image mappings for cancer diagnosis and prognosis

该项目旨在利用机器学习的最新进展来解决癌症管理中的严重问题-最直接的是前列腺癌。目前通过活检和组织学做出治疗决定的标准方法有两个关键限制;它是侵入性的和主观的/不一致的。我们将开发支持解决这两个问题的新解决方案的计算工具。具体来说，我们的目标是使非侵入性MRI成为主要的诊断工具。这将避免大量不必要的活检，这带来了改变生活的副作用的重大风险，只为边缘病例保留程序。我们还计划将MR信号与定量组织特征联系起来，从而实现一致的评估，从而做出更可靠的治疗决策。因此，MRI与患者结局（例如5-10年生存率）相关的数据不可用。然而，我们在获得i）相关MRI和组织学图像以及ii）相关组织学和患者结局方面具有独特的优势。结合起来，这些支持两步学习和估计过程：从MRI到组织学特征;从组织学特征到患者预后。这样的映射可以为临床决策提供宝贵的新信息，以及指导最大信息量的未来MRI协议的设计。该项目涉及工程挑战，需要在基于图像的机器学习技术的前沿进行创新：i）适应训练图像对齐中的不确定性; ii）学习模型输出中的不确定性的量化和可视化; iii）高维输入数据中的显著特征选择; iii）在基于图像的机器学习技术的前沿进行创新。iv）开发由隐式计算模型（例如神经网络）驱动的实验设计优化算法。我们基于深度学习的最新理念来应对这些挑战。我们为前列腺癌患者量身定制解决方案，解决当前的问题，同时也扩展到癌症成像和医学成像的相关任务。

This project aims to exploit recent advances in machine learning to address acute problems in cancer management - most directly prostate cancer. The current standard approach of making treatment decisions via biopsy and histology has two key limitations; it is invasive and subjective/inconsistent. We will develop the computational tools supporting new solutions that resolve both issues. Specifically, we aim to enable non-invasive MRI to become the primary diagnostic tool. This would avoid a large number of unnecessary biopsies, which carry significant risk of life-changing side-effects, reserving the procedure for only marginal cases. We also plan to relate MR signals to quantitative tissue features enabling consistent assessment and thus more reliable treatment decisions.The use of MRI in prostate cancer has become routine only in the last few years. Thus, data relating MRI to patient outcome (e.g. 5-10 year survival) is not available. However, we are uniquely positioned to obtain i) associated MRI and histology images, and ii) associated histology and patient outcome. In combination, these support a two-step learning and estimation process: from MRI to histological features; and from histological features to patient prognosis. Such mappings can provide invaluable new information for clinical decision making, as well as guide the design of maximally informative future MRI protocols. Such protocols will enable long-term data collection initiatives that support direct mappings from MRI to outcome.The project involves engineering challenges that demand innovations at the cutting edge of image-based machine learning technology: i) accommodating uncertainty in the alignment of training images; ii) quantification and visualization of uncertainty in the output of learned models; iii) salient feature selection in high-dimensional input data; iv) development of experiment design optimization algorithms driven by implicit computational models (such as neural networks). We build on the latest ideas in deep learning to address these challenges. We tailor solutions relevant to the immediate problems at hand in prostate cancer, but that extend to related tasks in cancer imaging and medical imaging in general.