Human-Centered Perceptual and Conceptual Classification of Biomedical Images

描述（由申请人提供）：生物医学图像的数量和重要性不断增加，但缺乏用于管理图像和理解其内容的有效计算解决方案。图像理解是推进这些努力的关键限制因素。主要的挑战仍然是在了解人类视觉系统的能力，生物医学成像和提取和利用领域专家的隐性知识。为了应对这些挑战，我们提出了一个创新的，多学科的方法，结合以用户为中心的设计，视觉感知和计算机成像研究的方法与领域专家进行互动，并引出和使用他们的外在和内在的知识。我们将使用一种新颖的上下文设计方法来检查皮肤病学图像，以发现图像的感知相关视觉内容与用户通过自然语言表达的概念理解之间的关系。分析用户的眼球运动和口头描述，以及映射到领域医学本体，将使我们能够将视觉数据与用户指定的语言模型相结合，以定义感知类别并通知图像分类。这是一个基本的和具有挑战性的数据到知识的问题，尚未得到解决。本研究将提供概念证明的价值，从领域专家通过多个感知相关的模式，以整合数据和知识模型，更好地理解图像，并可能有助于制定一个范式转变，我们如何概念化和开发生物医学信息系统，在一般情况下，隐性知识。

DESCRIPTION (provided by applicant): Biomedical images are ever increasing in quantity and importance yet effective computing solutions for managing images and understanding their content are lacking. Image understanding is a key limiting factor in advancing these endeavors. Major challenges remain in understanding the capabilities of the human visual system with respect to biomedical imaging and in extracting and utilizing tacit knowledge of domain experts. To meet these challenges, we propose an innovative, multidisciplinary approach which combines methods of user centered design, visual perception and computer imaging research to interact with domain experts and to elicit and use their extrinsic and intrinsic knowledge. We will use a novel contextual design approach to inspection of dermatology images to discover relationships between perceptually- relevant visual content of images and users' conceptual understanding as expressed through natural language. Analysis of users' eye movements and verbal descriptions, together with mapping to domain medical ontologies, will allow us to integrate visual data with a user-specified language model to define perceptual categories and inform image classification. This is a fundamental and challenging data to knowledge problem that has not been solved. This study will provide proof of concept of the value of eliciting tacit knowledge from domain experts through multiple perceptually relevant modes in order to integrate data and knowledge models for better image understanding and may help enact a paradigm shift in how we conceptualize and develop biomedical information systems, in general.