Improving the reliability of neural networks for medical imaging

在过去的十年里，机器学习和人工智能在分析医学成像数据方面取得了巨大的进步。这些进步主要是由先进的深度神经网络的发展推动的。然而，这种技术的一个主要问题是，这些模型并不健壮，而且它们的性能可能会受到数据特征变化的影响。具体来说，通常观察到，当神经网络应用于与用于训练模型的数据存在差异的数据时，其性能会显着下降。例如，深度神经网络在训练数据中代表性不足的少数民族数据上表现不佳。这构成了在诸如处理医学图像等高风险应用中实施此类模型的主要障碍——在这些应用中，错误的诊断可能会产生重大后果。因此，创造方法来提高神经网络的鲁棒性和可靠性，将允许安全有效地将该技术集成到医疗应用中，这有望为医疗保健带来显著的好处，例如减少工作人员的压力，加速和改进疾病诊断，并最终改善患者的预后。该项目的目的是提高神经网络用于医学图像研究的可靠性，例如x射线、CT和MRI图像。该项目的目标之一是创造方法，使神经网络模型能够量化其预测中的不确定性。这将使他们能够在预测可能出错时通知用户（称为“优雅失败”）。这将允许用户（如临床医生）判断是否在后续决策中包括模型的预测或丢弃模型的输出。此外，另一个目标是为神经网络创建一个工具，以清楚地解释它为什么根据给定的数据做出预测的原因。使用因果机器学习等技术，模型将能够解释哪些特征导致预测，增强其可解释性，以及解释哪些输入数据将导致失败。例如，目前许多基线模型错误地将图像中的运动伪影识别为病理，这可以使用因果机器学习进行研究。这项研究旨在使深度神经网络更安全、更可靠地用于医疗保健。这可能会产生重大影响，因为该技术有可能改善诊断和治疗，但由于所讨论的技术限制，其在临床工作流程中的整合目前受到阻碍。此外，这项研究的进展还将促进深度神经网络在医疗保健以外的其他高风险应用中的应用，如自动驾驶汽车。这个项目是多学科的，结合了计算机科学和医学数据的进步。因此，该项目属于EPSRC医疗保健技术主题。

Over the past decade, large strides have been made in machine learning and artificial intelligence for analysing medical imaging data. These advances have been primarily fuelled by development of advanced deep neural networks. However, a major problem with this technology is that these models are not robust, and their performance can be influenced by variations of the data characteristics. Specifically, it has been commonly observed that performance of neural networks degrades significantly when they are applied on data that present differences from the data that were used for training the models. For example, deep neural networks have been shown to perform poorly on data from people from ethnic minorities that were under-represented in the training data. This constitutes a major barrier to implementing such models in high-risk applications such as processing medical images - where an incorrect diagnosis could have major consequences. Therefore, creating methods to improve the robustness and reliability of neural networks will allow safe and effective integration of this technology in medical applications, which promises to deliver significant benefits to healthcare such as reducing the staff strain, accelerating and improving diagnosis of disease, and ultimately improving patient outcome.The aim of this project is to work on improving the reliability of neural networks for the study of medical images - such as X-ray, CT and MRI images. One aim of the project is to create methods to enable neural network models to quantify their own uncertainty in their predictions. This will provide them the ability to inform the user when it is likely that a prediction may be wrong (known as 'failing gracefully'). This would allow the user, such as clinicians, to judge whether to include the model's prediction in follow-up decision making or discard the model's output. Moreover, another aim is to create a tool for the neural network to clearly explain its reasoning for why it made a prediction based upon the data it was given. Using techniques such as causal machine learning, models will be able to explain what features led to a prediction, bolstering their interpretability, as well as explaining what input data will cause failures. For example, many baseline models currently misidentify motion artefacts in images as pathology, which could be studied using causal machine learning.This research aims to enable safer and more reliable use of deep neural networks for healthcare. This can have major impact as the technology has the potential to improve diagnosis and treatment, but its integration in clinical workflows is currently hindered due to the discussed technology limitations. Furthermore, advancements from this research will also facilitate the implementation of deep neural networks in other high-risk applications beyond healthcare - such as self-driving vehicles.This project is multi-disciplinary, combining advances in computer science with medical data. Therefore, this project falls within the EPSRC Healthcare Technologies theme.