Thunold Håvard Horgen, Riegler Michael A, Yazidi Anis, Hammer Hugo L
Department of Compute Science, Faculty of Technology, Art and Design, Oslo Metropolitan University, 0176 Oslo, Norway.
Department of Holistic Systems, SimulaMet, 0176 Oslo, Norway.
Diagnostics (Basel). 2023 Nov 9;13(22):3413. doi: 10.3390/diagnostics13223413.
An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data are presented as images, identifying properties that characterize a disease becomes far more challenging. A common strategy involves extracting features from the images and analyzing their occurrence in healthy versus pathological images. A limitation of this approach is that the ability to gain new insights into the disease from the data is constrained by the information in the extracted features. Typically, these features are manually extracted by humans, which further limits the potential for new insights. To overcome these limitations, in this paper, we propose a novel framework that provides insights into diseases without relying on handcrafted features or human intervention. Our framework is based on deep learning (DL), explainable artificial intelligence (XAI), and clustering. DL is employed to learn deep patterns, enabling efficient differentiation between healthy and pathological images. Explainable artificial intelligence (XAI) visualizes these patterns, and a novel "explanation-weighted" clustering technique is introduced to gain an overview of these patterns across multiple patients. We applied the method to images from the gastrointestinal tract. In addition to real healthy images and real images of polyps, some of the images had synthetic shapes added to represent other types of pathologies than polyps. The results show that our proposed method was capable of organizing the images based on the reasons they were diagnosed as pathological, achieving high cluster quality and a rand index close to or equal to one.
诊断的一个重要部分是深入了解表征疾病的特征。机器学习已被用于此目的，例如，在基因组学中识别生物标志物。然而，当患者数据以图像形式呈现时，识别表征疾病的特征变得更具挑战性。一种常见策略是从图像中提取特征，并分析它们在健康图像与病理图像中的出现情况。这种方法的一个局限性在于，从数据中获得对疾病新见解的能力受到提取特征中信息的限制。通常，这些特征是由人工手动提取的，这进一步限制了获得新见解的潜力。为了克服这些局限性，在本文中，我们提出了一种新颖的框架，该框架无需依赖手工制作的特征或人工干预即可深入了解疾病。我们的框架基于深度学习（DL）、可解释人工智能（XAI）和聚类。深度学习用于学习深度模式，从而能够有效地区分健康图像和病理图像。可解释人工智能（XAI）将这些模式可视化，并引入了一种新颖的“解释加权”聚类技术，以全面了解多个患者的这些模式。我们将该方法应用于胃肠道图像。除了真实的健康图像和息肉的真实图像外，一些图像还添加了合成形状以表示除息肉之外的其他类型的病变。结果表明，我们提出 的方法能够根据图像被诊断为病理图像的原因对图像进行组织，实现了高聚类质量和接近或等于1的兰德指数。
