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Mga-Net: Multi-Scale Guided Attention Models for an Automated Diagnosis of Idiopathic Pulmonary Fibrosis (IPF)

机译:MGA-NET:多尺度引导注意力模型,用于自动诊断特发性肺纤维化(IPF)

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We propose a Multi-scale, domain knowledge-Guided Attention model (MGA-Net) for a weakly supervised problem-disease diagnosis with only coarse scan-level labels. The use of guided attention models encourages the deep learning-based diagnosis model to focus on the area of interests (in our case, lung parenchyma), at different resolutions, in an end-to-end manner. The research interest is to diagnose subjects with idiopathic pulmonary fibrosis (IPF) among subjects with interstitial lung disease (ILD) using an axial chest high resolution computed tomography (HRCT) scan. Our dataset contains 279 IPF patients and 423 non-IPF ILD patients. The network's performance was evaluated by the area under the receiver operating characteristic curve (AUC) with standard errors (SE) using stratified five-fold cross validation. We observe that without attention modules, the IPF diagnosis model performs unsatisfactorily (AUC ± SE = 0.690 ± 0.194); by including unguided attention module, the IPF diagnosis model reaches satisfactory performance (AUC ± SE = 0.956 ± 0.040), but lack explainability; when including only guided high-or medium-resolution attention, the learned attention maps highlight the lung areas but the AUC decreases; when including both high- and medium-resolution attention, the model reaches the highest AUC among all experiments (AUC ± SE = 0. 971 ± 0.021) and the estimated attention maps concentrate on the regions of interests for this task. Our results suggest that, for a weakly supervised task, MGA-Net can utilize the population-level domain knowledge to guide the training of the network in an end-to-end manner, which increases both model accuracy and explainability.
机译:我们提出了一种多规模的域知识引导的注意模型(MGA-NET),用于仅具有粗扫描级标签的弱监督的问题疾病诊断。使用引导的注意力模型鼓励基于深度学习的诊断模型,专注于以端到端的方式以不同的分辨率在不同的分辨率下兴趣(在我们的案例中,肺部)。研究兴趣是使用轴向胸高分辨率计算断层扫描(HRCT)扫描,诊断具有正式肺病(ILD)的特发性肺纤维化(IPF)的受试者。我们的数据集包含279名IPF患者和423名非IPF患者。网络的性能由接收器操作特性曲线(AUC)下的区域评估,使用分层五倍交叉验证具有标准误差(SE)。我们观察到没有注意力模块,IPF诊断模型表现不令人满意(AUC±SE = 0.690±0.194);通过包括无导力的注意模块,IPF诊断模型达到令人满意的性能(AUC±SE = 0.956±0.040),但缺乏解释性;当包括仅引导高或中分辨率的关注时,所学知的注意图突出了肺区,但AUC降低;当包括高分辨率和中分辨率的关注时,该模型在所有实验中达到最高的AUC(AUC±SE = 0.971±0.021),估计的注意力图集中在此任务的利益区域。我们的结果表明,对于弱监督任务,MGA-Net可以利用人口级域知识来指导以端到端的方式引导网络的培训,这增加了模型准确性和解释性。

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