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

开发和使用新的衍生数据，以促进乳腺筛查人工智能工具的优化和评估

• 批准号: 2755033
• 金额: --
• 依托单位: University of Surrey
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2755033
• 关键词: Development; use; new; derived; data

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.

该博士项目将使用技术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，以了解这对用于人工智能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人工智能工具的性能。这将涉及对内部算法的再训练和随后的验证，以评估不同的训练和验证数据集所产生的效果。预计除了使用内部或开源算法外，该项目还将利用各小组与谷歌Health的现有合作，在谷歌健康乳房人工智能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，以获得新数据，以评估基于人工智能的医学成像工具。这需要以一种非常适合博士项目的方式开发新知识和基础研究。该项目将计量分析和数据科学技能与医学成像结合起来，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。