Leveraging language understanding to decrease uncertainty measurements in medical image analysis

根据UKRI的投资组合和优先事项，该项目属于EPSRC医学成像-包括医学图像和视觉计算研究领域。在快速发展的医学图像分析领域，对强大和可靠的工具的需求日益增长，以帮助临床决策。成像数据的日益复杂，加上医学诊断中固有的不确定性，概述了在医学图像分析中更好地理解不确定性测量的重要性。该研究项目旨在通过深入研究医学图像分析中的不确定性领域来解决这一关键差距，最终目标是使用额外的语言学信息来提高诊断结果的准确性和可靠性。总的来说，医学图像处理的现代进步源于人工智能(AI)的使用，人工智能(AI)从一组训练数据中学习。一旦模型被训练，然后使用新的未见数据来查询它，以便我们可以评估它的性能，并评估将该模型部署到现实世界中使用。在2D和3D图像的上下文中，这种人工智能的术语被称为计算机视觉，包括计算机如何理解成像数据的语义。计算机视觉应用的快速发展一直在利用来自其他深度学习领域的强大工具，例如自然语言处理(NLP)，它使用人工智能来理解语言信息并对其进行操作。因此，培训程序和模式正在改进。例如，变形金刚是一种理解语言的尖端方法，在培训时利用辅助问题。此外，现有的研究可以突出扫描的异常区域，而不需要使用标记的训练数据来表示“该区域是异常的并且包含胶质瘤”。这被称为无监督学习，并且通常涉及使用诸如VAE、GaN或扩散模型的生成模型来从图像的压缩表示重建图像。因此，这使我们能够依靠从医学角度可以获得的有限的异常数据。评估医学图像分析中现有不确定性测量的稳健性。量化不确定性对诊断准确性和临床决策结果的影响。探索不确定性感知模型在不同医学成像模式中的应用，如MRI、CT和X光。研究添加自然语言对临床诊断准确性不确定性的影响。

As per the UKRI portfolio and priorities, this project falls within the EPSRC Medical imaging -including medical image and vision computing research area.In the rapidly advancing field of medical image analysis, there is a growing need for robust and reliable tools to aid in clinical decision-making. The increasing complexity of imaging data, coupledwith the inherent uncertainty in medical diagnostics, outlines the significance of a better understanding of uncertainty measurements in medical image analysis. The research project aims to address this critical gap by delving into the realm of uncertainty in medical image analysis, withthe ultimate goal of improving the accuracy and reliability of diagnostic outcomes using additionallinguistic information.In general, modern advancements in medical image processing have derived from the use ofartificial intelligence (AI) which learns from a set of training data. Once the model has beentrained, it is then queried with new unseen data so that we can assess it's performance and evaluate deploying the model to use in the real-world. The term for this AI in the context of 2D and 3D images is called computer vision and encompasses how computers understand the semantics of imaging data.Rapidly growing advancements in computer vision applications have been leveraging powerfultools from other areas of deep learning such as from Natural Language Processing (NLP) which uses AI to understand and act on language information. As a result, training procedures and models are improving. For example, transformers which are a cutting edge methodology to understand language, utilise auxiliary problems when training. These are alternative sub problems that the model is trained on that have been proven to improve the performance on the original primary problem.Additional existing research can highlight anomalous regions of a scan without using labelledtraining data to say "this region is anomalous and contains a glioma". This is referred to asunsupervised learning and generally involves using a generative model such as a VAE, GAN ordiffusion model to recreate the image from a compressed representation of the image. Consequently this allows us to lean in to the limited anomalous data that may be available from a medical perspective.Aims1. Evaluate the robustness of existing uncertainty measurements in medical image analysis.2. Quantify the impact of uncertainty on diagnostic accuracy and clinical decision outcomes.3. Explore the application of uncertainty-aware models in diverse medical imaging modalities,such as MRI, CT, and X-ray.4. Investigate the consequence adding natural language has on the uncertainty on clinical diagnostic accuracy.