论文标题

使用代理的多发性基于内容的医学图像检索

Multimorbidity Content-Based Medical Image Retrieval Using Proxies

论文作者

Xing, Yunyan, Meyer, Benjamin J., Harandi, Mehrtash, Drummond, Tom, Ge, Zongyuan

论文摘要

基于内容的医疗图像检索是一个重要的诊断工具,可改善计算机辅助诊断系统的解释性,并为医疗保健专业人员提供决策支持。医学成像数据,例如放射学图像,通常是多种疾病。单个样本可能存在多种病理。因此,必须针对多标签场景设计用于医疗域的图像检索系统。在本文中,我们提出了一种新型的多标签度量学习方法,可用于分类和基于内容的图像检索。这样,我们的模型能够通过预测疾病的存在来支持诊断,并通过返回具有与用户相似的病理含量的样本来为这些预测提供证据。实际上,检索到的图像也可能伴随着病理报告,进一步有助于诊断过程。我们的方法利用代理特征向量,使能够有效学习可靠的特征空间,在该空间中,特征向量之间的距离可以用作衡量这些样品相似性的度量。与现有的基于代理的方法不同,培训样本能够分配给跨越多个类标签的多个代理。此多标签代理分配会导致特征空间编码医学成像数据中存在的疾病之间的复杂关系。我们的方法优于最先进的图像检索系统和一组基线方法。我们证明了我们在两个多发性放射学数据集上对分类和基于内容的图像检索的方法的功效。

Content-based medical image retrieval is an important diagnostic tool that improves the explainability of computer-aided diagnosis systems and provides decision making support to healthcare professionals. Medical imaging data, such as radiology images, are often multimorbidity; a single sample may have more than one pathology present. As such, image retrieval systems for the medical domain must be designed for the multi-label scenario. In this paper, we propose a novel multi-label metric learning method that can be used for both classification and content-based image retrieval. In this way, our model is able to support diagnosis by predicting the presence of diseases and provide evidence for these predictions by returning samples with similar pathological content to the user. In practice, the retrieved images may also be accompanied by pathology reports, further assisting in the diagnostic process. Our method leverages proxy feature vectors, enabling the efficient learning of a robust feature space in which the distance between feature vectors can be used as a measure of the similarity of those samples. Unlike existing proxy-based methods, training samples are able to assign to multiple proxies that span multiple class labels. This multi-label proxy assignment results in a feature space that encodes the complex relationships between diseases present in medical imaging data. Our method outperforms state-of-the-art image retrieval systems and a set of baseline approaches. We demonstrate the efficacy of our approach to both classification and content-based image retrieval on two multimorbidity radiology datasets.

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