Development and use of new and derived data to facilitate the optimisation and evaluation of breast screening AI tools

这个博士项目将使用技术来改变未经处理的图像，使其看起来像是用较低剂量或不同光束质量或X射线探测器捕获的，或者使用不同制造商的图像处理软件进行处理，以生成额外的数据来补充OMI-DB。它还将开发图像处理软件，以了解这对用于AI工具开发的图像的影响。该项目将涉及使用这些数据来调查和评估图像中的不同因素如何混淆或影响AI工具的性能。这将涉及重新训练内部算法和随后的验证，以评估改变训练和验证数据集所产生的效果。除了使用内部或开源算法外，该项目还将利用团队与Google Health的现有合作，在Google Health Breast AI工具中调查这些相同的因素。该项目将开发基于物理的方法，以获得新的数据来评估基于AI的医学成像工具。这需要以非常适合博士项目的方式发展新知识和基础研究。该项目将数据分析和数据科学技能与医学成像结合在一起，并涉及与来自多个不同学科的专家合作，为候选人在医学研究或数据密集型计量领域的职业生涯做好准备。

This PhD project will use techniques to alter an unprocessed image to make it appear as if it was captured with lower dose or different beam quality or x-ray detector, or processed using different manufactures image processing software to generate additional data to supplement OMI-DB. It will also develop image processing software to understand the effect of this on the images that are used for AI tool development. This project will involve the use of this data to investigate and evaluate how varying factors in the images may confound or affect an AI tools performance. This will involve retraining in-house algorithms and subsequent validation to evaluate the resulting effects that varying the training and validation datasets have. Is it anticipated that in addition to the use of in-house or open-source algorithms the project will involve utilising the groups' existing collaboration with Google Health to investigate these same factors in the Google Health Breast AI tool.This project will develop physics-based methodology to derive new data to assess AI based tools for medical imaging. This requires the development of new knowledge and fundamental research in a way which is well-suited to a PhD project. The project brings together metrological analysis and data science skills with medical imaging, and involves working with specialists from several different disciplines, preparing the candidate for a career in either medical research or data-intensive metrology.