Health Data Science CDT

该项目旨在开发新的计算方法来分析胎盘组织学图像，以更好地了解产妇健康结果。胎盘在胎儿发育中起着至关重要的作用，可以为胎儿和母亲的健康提供有价值的见解。然而，胎盘分析仍然具有挑战性，由于器官的空间和时间的复杂性和异质性。该研究将利用深度学习和计算机视觉技术从胎盘组织的整个幻灯片图像（wsi）中提取有生物学意义的信息。具体而言，该项目将现有的监督3阶段管道（核细胞-组织）扩展到4阶段自我监督学习管道（核细胞-组织-病理），以生成胎盘组织学的低维表示，无需依赖专家注释即可捕获特征。这种方法有潜力揭示模式和表型，可能不明显通过传统的分析方法，将允许胎盘组织的高通量分析。下游这可以用来表征胎盘病理和了解胎盘变异如何影响孕产妇健康。该项目的主要目标是：1。为胎盘组织学图像开发一个4阶段的自我监督表示学习管道，扩展wsi中核、细胞和组织分类的现有工作，包括病理鉴定，使胎盘组织的正常变化和病理变化的表征成为可能。开发基于图的表示学习方法，以压缩和解释WSI数据，使下游分析能够聚类胎盘表示。利用纵向队列数据研究胎盘组织学表征与预测孕产妇健康结局的临床相关性。研究方法的新颖性体现在几个方面。本计划将发展自我监督学习技术，以研究胎盘病理学领域。通过避免对专家注释的依赖，开发的方法有可能揭示公正的生物模式。该项目引入了一个多阶段可解释的管道，其中表征胎盘（核细胞-组织病理学）的每个阶段都可以独立理解和分析，具有潜在的基础组织学模型应用。该项目将开发一种新的图形自编码器架构，旨在压缩WSI信息，在保留结构信息的同时实现高效的下游分析。总的来说，该项目通过自动化胎盘分析功能来提取生物学上有意义的信息，具有重大影响的潜力。这可以提高对胎盘病理及其与产妇健康和产后风险的关系的认识。该项目属于EPSRC医疗技术主题，特别是“图像和视觉计算”和“生物信息学”研究领域。

This project aims to develop novel computational methods for analysing placental histology images to better understand maternal health outcomes. The placenta plays a crucial role in fetal development and can provide valuable insights into both fetal and maternal health. However, placental analysis remains challenging due to the organ's spatial and temporal complexity and heterogeneity. The research will leverage deep learning and computer vision techniques to extract biologically meaningful information from whole slide images (WSIs) of placental tissue.Specifically, the project will extend an existing supervised 3-stage pipeline (nuclei-cell-tissue) to a 4-stage self-supervised learning pipeline (nuclei-cell-tissue-pathology) to generate low- dimensional representations of placental histology that capture features without relying on expert annotations. This approach has the potential to reveal patterns and phenotypes that may not be apparent through traditional analysis methods and will allow for high- throughput analysis of placental tissue. Downstream this can be used to characterise placenta pathologies and understand how placenta variation affects maternal health.The key objectives of the project are:1. Develop a 4-stage self-supervised representation learning pipeline for placentalhistology images, extending existing work on nuclei, cell, and tissue classification in WSIs to include pathology identification, enabling characterisation of normal variation and pathological changes of placental tissue.2. Develop methods for graph-based representation learning to compress and interpret WSI data to enable downstream analysis to cluster placentas representations.3. Investigate the clinical relevance of placental histology representations for predicting maternal health outcomes using longitudinal cohort data.The novelty of the research methodology is in several aspects. This project will develop self- supervised learning techniques to the understudied domain of placental pathology. By avoiding reliance on expert annotations, the methods developed have the potential to reveal unbiased biological patterns. The project introduces a multistage interpretable pipeline, where each stage of characterising the placenta (nuclei-cell-tissue-pathology) can be understood and analysed independently, with potential applications for a foundation histology model. The project will develop a new graph autoencoder architecture, designed to compress the WSI information to enable efficient downstream analysis while preserving structural information.Overall, the project has the potential for significant impact by automating placental analysis capabilities to extract biologically meaningful information. This could lead to improved understanding of placental pathologies and their relationship to maternal health and post- partum risks.This project falls within the EPSRC Healthcare Technologies theme, specifically the 'Image and Vision Computing' and 'Biological Informatics' research areas.