Abnormality detection and characterisation in neuroimaging using deep learning

使用深度学习进行神经影像异常检测和表征

• 批准号: 2444278
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2444278
• 关键词: Abnormality; detection; characterisation; neuroimaging; using

The objective of this project is to develop a decision-making tool that identifies abnormalities on MRI brain scans using deep learning, for triage and identifying false negatives in previously reported scans. The project will use a retrospective dataset of >100,000, minimally processed, MRI scans from three different sites, which have been labelled by a combination of consultant neuroradiologists and a validated NLP algorithm. A classification model has been built and validated on this data, using a supervised CNN (DenseNet-121), with high accuracy (AUC 0.943).One aim is to extend this work by revisiting some of the automatic NLP labelling. It is currently unclear the optimal way to train and utilise BERT-based models for the automatic labelling of radiology reports, specificially to neuroradiology reports in the UK. Most existing approaches rely on fine-tuning a classification head on large pre-trained language models. Preliminary work seems to suggest that models trained from scratch or those that continue pretraining on neuroradiology reports would outperform these models. Following this, it would be necessary to prove whether improvements in label classification led to a meaningful difference in downstream image classification.Another aim of this project is to characterise abnormalities by normative modelling using deep learning. Normative modelling seeks to gain a representation of healthy anatomy by training on healthy controls, any significant deviation from which would be considered abnormal. The approach is fundamentally different from supervised CNN approaches, which instead attempts to generate features that best separate healthy and abnormal examinations.

该项目的目标是开发一种决策工具，使用深度学习来识别MRI脑扫描的异常，用于分类和识别先前报告的扫描中的假阴性。该项目将使用来自三个不同地点的10万英镑、经过最低限度处理的MRI扫描的追溯数据集，这些扫描已经由顾问神经放射科医生和经过验证的NLP算法相结合进行标记。一个分类模型已经被建立和验证在这些数据上，使用有监督的CNN(DenseNet-121)，具有高精度(AuC 0.943)。一个目的是通过重新访问一些自动的自然语言标签来扩展这项工作。目前还不清楚训练和使用基于BERT的模型的最佳方式，以自动标记放射学报告，特别是英国的神经放射学报告。大多数现有的方法依赖于在大型预先训练的语言模型上微调分类头。初步工作似乎表明，从头开始训练的模型或那些继续根据神经放射学报告进行预培训的模型会比这些模型表现更好。接下来，有必要证明标签分类的改进是否导致了下游图像分类的有意义的差异。本项目的另一个目标是通过使用深度学习的标准化建模来表征异常。标准建模试图通过对健康对照的训练来获得健康解剖学的代表，任何明显偏离这一点将被认为是异常的。这种方法从根本上不同于有监督的CNN方法，后者试图生成最好地区分健康和异常检查的特征。