Probabilistic deep learning approaches in medical imaging

在当今的医学中，成像数据的使用在护理的各个方面都发挥着不可或缺的作用，并跨越了大多数医疗领域。对这些数据的有效分析至关重要，需要准确、可靠和高效的工具。人工智能（AI）已成为一种变革性的解决方案，广泛应用于各种成像模式和疾病。人工智能显著提高了图像分析的精度和效率，同时减轻了临床医生的负担。然而，人工智能模型仍然面临着一个挑战，即难以在不同的数据源（如不同的医院或成像设备）中进行泛化，并适应不同的诊断任务。解决这些挑战的一个有希望的途径是将统计和概率方法应用于AI模型。这些方法可以提高模型的鲁棒性和可靠性，但尚未得到充分的探索，特别是在医学成像领域。这种研究差距形成了我的哲学博士项目的基础。我的项目将专注于开发和评估为医学成像量身定制的概率深度学习工具，使用频率论和贝叶斯统计方法。这包括建立新的模型，这些模型对分布外的数据具有鲁棒性，可以信任，并且可以区分因果关系，相关性和混杂效应。一个特别的重点将放在模型的发展，可以关联的信心估计或分布到他们的每一个预测。到目前为止，大多数经典的深度学习模型只提供了预测的点估计，并且“不知道他们不知道什么”，当他们看到来自不同分布的图像时，这可能特别有问题。量化不确定性是临床医生信任模型的关键，特别是在医疗保健等决策敏感的环境中。尽管如此，现有的量化不确定性的医学成像模型，如贝叶斯神经网络或蒙特卡罗丢弃方法，通常会产生显著更高的计算成本，因为它们涉及计算每个模型权重的分布并分别训练一系列具有不同激活的网络。因此，本项目的目标是开发这样的模型，可以可靠地估计其不确定性，同时保持预测精度和低计算成本。我将主要使用肿瘤患者的多模式正电子发射断层扫描和计算机断层扫描（PET/CT）数据，以开发和测试这些模型。这种类型的数据将特别受益于不确定性感知模型，因为存在广泛的扫描仪间可变性以及临床医生对扫描的解释的可变性。这将使更广泛的深度学习研究社区受益，因为代码将是开源的，方法将共享，以及通过提供更安全，更值得信赖的方法，临床也将受益。例如，这在使用PET/CT肿瘤分割来指导放射治疗中将是重要的，因为具有跨预测的肿瘤区域的不确定性的图将避免靶向任何潜在的健康区域，例如在边缘处，这是众所周知的难以分割。GE Healthcare公司将作为行业合作伙伴参与该项目，并将帮助提供我可以使用的精选数据集。该项目符合EPSRC的战略和研究领域。具体而言，该项目福尔斯以下EPSRC研究领域：-人工智能技术-图像和视觉计算-医学成像-统计和应用概率。

In medicine today, the use of imaging data plays an indispensable role across all aspects of care and spans most medical domains. The effective analysis of this data is paramount, requiring accurate, reliable, and efficient tools. Artificial Intelligence (AI) has emerged as a transformative solution, extensively applied across diverse imaging modalities and diseases. AI has notably enhanced the precision and efficiency of image analysis while reducing the burden on clinicians. However, a challenge persists in the form of AI models struggling to generalize across disparate data sources, such as different hospitals or imaging devices, and adapting to different diagnostic tasks. One promising avenue to address these challenges is the application of statistical and probabilistic approaches to AI models. These approaches can enhance model robustness and reliability, but have yet to be fully explored, particularly within the realm of medical imaging. This research gap forms the basis of my DPhil project. My project will focus on the development and evaluation of probabilistic deep learning tools tailored to medical imaging, using methods from both frequentist and Bayesian statistics. This includes building novel models which are robust to out-of-distribution data, can be trusted, and from which causal, correlative, and confounding effects can be distinguished. A particular focus will be placed on the development of models which can associate a confidence estimate or distribution to each of their predictions. To date, most classical deep learning models only provide a point-estimate of their prediction, and "don't know what they don't know", which can be particularly problematic when they are presented with images from a different distribution to those with which they were trained. Quantifying uncertainty is key to models being trusted by clinicians, especially in decision-sensitive contexts such as healthcare. Despite this, existing medical imaging models which quantify uncertainty, such as Bayesian neural networks or Monte Carlo dropout methods, often incur significantly higher computational costs, as they involve calculating a distribution over each of the models' weights and training a series of networks with different activations for each layer respectively. This project will therefore aim to develop such models which can reliably estimate their uncertainty while maintaining prediction accuracy and low computational cost. I will primarily use multimodal Positron Emission Tomography and Computed Tomography (PET/CT) data of patients with tumours in order to develop and test these models. This type of data would particularly benefit from uncertainty-aware models as there is extensive inter-scanner variability as well as variability in the interpretation of the scans by clinicians. This would benefit the wider deep learning research community, as code would be open-sourced and methods shared, as well as the clinic, by providing safer and more trustworthy methods. For instance, this would be important in the use of PET/CT tumour segmentation to guide radiotherapy, as having a map of the uncertainty across the predicted tumour area would avoid targeting of any potentially healthy areas, eg at the margins, which are notoriously harder to segment. The company GE Healthcare will be involved in the project as the industrial partner and will also help provide curated dataset(s) that I can work with. This project aligns with EPSRC's strategies and research areas. Specifically, this project falls within the following EPSRC research areas: - Artificial intelligence technologies - Image and vision computing - Medical imaging - Statistics and applied probability.