Causal Generative Modelling for Identifying and Alleviating Biases of Neural Networks in Medical Imaging
用于识别和减轻医学成像中神经网络偏差的因果生成模型
基本信息
- 批准号:2721977
- 负责人:
- 金额:--
- 依托单位:
- 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:
- 关键词:
项目摘要
Medical image analysis is undergoing a transformation with the emergence of Deep Neural Networks (DNNs), a type of machine learning models that are increasingly being trialled into clinical workflows as tools, for example to assist pathology detection. The diversity of medical images, however, resulting from the variety of patient populations, acquisition scanners and protocols, poses a challenge for ensuring reliable performance after model deployment. Because medical imaging databases are of limited size, they commonly do not cover this heterogeneity. As a result, training data can include biases, for example images acquired using a specific type of scanner or primarily from patients of specific age. These biases can be inherited by a model trained on this data, which may underperform if deployed to process data with different characteristics. This can have serious consequences in healthcare. The current generation of DNNs lacks dependable mechanisms for detecting and alerting users when predictions may be unreliable.Aim of this project is to create safer and more dependable machine learning for medical imaging. Our approach is to develop Causal Generative Models (CGMs) capable of explicitly capturing relationships between factors that either influence or should not influence model predictions. By doing so, we can pinpoint the specific data characteristics responsible for biases and sub-optimal model predictions. This knowledge can then be used to alert a user of possible sub-optimal model operation, or use this knowledge to alleviate the influence of such data characteristics, to obtain fairer and more reliable models.To this end, the project has the following specific objectives:a) The first objective is to develop CGMs for modelling the characteristics of a database and identifying any biases therein. Medical image datasets typically contain correlations between variables such as age, sex, disease severity and used imaging equipment among others, with certain combinations occurring more often than others. We will develop novel causality-based methods for modelling the relations between these variables, how they affect image appearance, and identify if such relations are desirable or artifacts of spurious correlations in the data. Causal inference will then enable synthesis of artificial images, so called counter-factuals, for characteristics that are under-represented or missing from the original database. Although common in other areas of health research such as genetics and epidemiology, causal inference has seen far less use in the context of imaging.b) The second objective is to develop methods for identifying biases inherited by a model that has already been trained for a task of interest (e.g. detection of a pathology), potentially due to a biased training database. This is critical in order to determine how well it will perform upon deployment, for example to ensure fair and reliable operation under different patient demographics or image quality.c) The final objective is to develop techniques for alleviating any biases identified in a pre-trained model. This complements the previous objectives and combines their output in a single framework: Having identified biases of a model (b), we will use a Causal Generative Model of the data (a) to generate synthetic but realistic data of the characteristics for which the model tends to underperform. This synthetic data will be used for adapting the model, enhancing its performance for more reliable operation. This project falls within the EPSRC Medical Imaging research area and the Healthcare Technologies theme.The proposed research will generate novel techniques based on causal inference for the mitigation of bias in medical image analysis, which will lead to fair and more trustworthy models, necessary for adoption of such tools in real-world clinical settings.
随着深度神经网络(DNN)的出现,医学图像分析正在经历一场变革,DNN是一种机器学习模型,越来越多地被试用到临床工作流程中作为工具,例如用于辅助病理检测。然而,由于患者人群、采集扫描仪和协议的多样性,医学图像的多样性对确保模型部署后的可靠性能提出了挑战。由于医学成像数据库的规模有限,它们通常不涵盖这种异质性。因此,训练数据可能包括偏差,例如使用特定类型的扫描仪或主要从特定年龄的患者获取的图像。这些偏差可以由在这些数据上训练的模型继承,如果部署到处理具有不同特征的数据,则可能表现不佳。这可能对医疗保健产生严重后果。当前一代DNN缺乏可靠的机制来检测和提醒用户预测可能不可靠。该项目的目的是为医学成像创建更安全,更可靠的机器学习。我们的方法是开发因果生成模型(CGMs),能够明确地捕捉影响或不应该影响模型预测的因素之间的关系。通过这样做,我们可以确定导致偏差和次优模型预测的特定数据特征。这些知识可以用来提醒用户可能的次优模型操作,或使用这些知识来减轻这些数据特征的影响,以获得更公平和更可靠的模型。为此,该项目有以下具体目标:a)第一个目标是开发CGM,用于模拟数据库的特征并识别其中的任何偏差。医学图像数据集通常包含诸如年龄、性别、疾病严重程度和使用的成像设备等变量之间的相关性,其中某些组合比其他组合更经常发生。我们将开发新的基于容差的方法来建模这些变量之间的关系,它们如何影响图像外观,并确定这种关系是否是可取的或伪相关的数据。然后,因果推理将能够合成人工图像,即所谓的反事实,用于原始数据库中代表不足或缺失的特征。虽然在其他健康研究领域(如遗传学和流行病学)中很常见,但因果推断在成像领域的应用却少得多。B)第二个目标是开发用于识别模型继承的偏差的方法,该模型已经针对感兴趣的任务(例如,病理检测)进行了训练,可能是由于有偏差的训练数据库。这对于确定其在部署时的性能如何至关重要,例如,以确保在不同的患者人口统计数据或图像质量下的公平和可靠的操作。c)最终目标是开发用于减轻在预先训练的模型中识别的任何偏差的技术。这补充了前面的目标,并将其输出结合在一个单一的框架中:在确定了模型(B)的偏差之后,我们将使用数据的因果生成模型(a)来生成模型往往表现不佳的特征的合成但现实的数据。这些合成数据将用于调整模型,提高其性能,以实现更可靠的操作。该项目属于EPSRC医学成像研究领域和医疗保健技术主题。福尔斯提出的研究将产生基于因果推理的新技术,以减轻医学图像分析中的偏见,这将导致公平和更值得信赖的模型,这是在现实世界的临床环境中采用此类工具所必需的。
项目成果
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其他文献
吉治仁志 他: "トランスジェニックマウスによるTIMP-1の線維化促進機序"最新医学. 55. 1781-1787 (2000)
Hitoshi Yoshiji 等:“转基因小鼠中 TIMP-1 的促纤维化机制”现代医学 55. 1781-1787 (2000)。
- DOI:
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LiDAR Implementations for Autonomous Vehicle Applications
- DOI:
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2021 - 期刊:
- 影响因子:0
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吉治仁志 他: "イラスト医学&サイエンスシリーズ血管の分子医学"羊土社(渋谷正史編). 125 (2000)
Hitoshi Yoshiji 等人:“血管医学与科学系列分子医学图解”Yodosha(涉谷正志编辑)125(2000)。
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Effect of manidipine hydrochloride,a calcium antagonist,on isoproterenol-induced left ventricular hypertrophy: "Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,K.,Teragaki,M.,Iwao,H.and Yoshikawa,J." Jpn Circ J. 62(1). 47-52 (1998)
钙拮抗剂盐酸马尼地平对异丙肾上腺素引起的左心室肥厚的影响:“Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,
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