Clinical Image Retrieval: User needs assessment toolbox development & evaluation

描述（由申请人提供）： 近年来，数字成像技术的进步已经导致在医院、医疗系统和互联网上创建和存储的数字图像的数量大幅增长。有效的医学图像检索系统可以在教学、科研、诊断和治疗中发挥重要作用。历史上使用基于文本的方法检索图像。与图像相关的注释的质量会降低基于文本的图像检索的有效性。尽管最近的进展，纯粹的基于内容的图像检索技术明显落后于他们的文本同行在他们的能力，以捕捉用户的查询的语义本质。初步的研究表明，一个更有前途的方法是自适应联合收割机这些互补的技术，以适应用户和他们的信息需求。然而，为了使这些方法取得成功，研究人员除了对相关临床领域有全面的了解外，还需要提高她的计算技能。这条通往独立的道路（K99/R 00）资助申请描述了一个培训和职业发展计划，该计划将允许候选人，俄勒冈州健康与科学大学医学信息学的NLM博士后研究员实现这些目标。培训部分将在W博士的指导下进行。Hersh和Gorman博士（用户研究）。Fuss博士（放射医学）和Erdogmus博士（机器学习）在他们的专业领域提供额外的指导。 这个独立之路（K99/R 00）项目的长期目标是通过更好地理解用户需求和提出适应性方法来改善视觉信息检索，以缩小语义差距。在奖励期间，活动将集中在以下具体目标：（1）了解放射肿瘤学中新手和专家用户的图像检索需求，并制定评估的金标准;（2）开发语义、多模态图像检索算法;（3）执行基于用户的放射肿瘤学自适应图像检索评估;（4）扩展已开发的技术，以创建病理学多模态图像检索系统

DESCRIPTION (provided by applicant): Advances in digital imaging technologies have led to a substantial growth in the number of digital images being created and stored in hospitals, medical systems, and on the Internet in recent years. Effective medical image retrieval systems can play an important role in teaching, research, diagnosis and treatment. Images were historically retrieved using text-based methods. The quality of annotations associated with images can reduce the effectiveness of text-based image retrieval. Despite recent advances, purely content- based image retrieval techniques lag significantly behind their textual counterparts in their ability to capture the semantic essence of the user's query. Preliminary research suggests that a more promising approach is to adaptively combine these complementary techniques to suit the user and their information needs. However, for these approaches to succeed, the researcher needs to enhance her computational skills in addition to acquiring a comprehensive understanding of the relevant clinical domain. This Pathway to Independence (K99/R00) grant application describes a training and career development plan that will allow the candidate, an NLM postdoctoral fellow in Medical Informatics at Oregon Health & Science University to achieve these objectives. The training component will be carried out under the mentorship of Dr. W. Hersh with Dr. Gorman (user studies). Dr. Fuss (radiation medicine) and Dr. Erdogmus (machine learning) providing additional mentoring in their areas of expertise. The long-term goal of this Pathway to Independence (K99/R00) project is to improve visual information retrieval by better understanding user needs and proposing adaptive methodologies for multimodal image retrieval that will close the semantic gap. During the award period, activities will be focused on the following specific aims: (1) Understand the image retrieval needs of novice and expert users in radiation oncology and develop gold standards for evaluation; (2) Develop algorithms for semantic, multimodal image retrieval; (3) Perform user based evaluation of adaptive image retrieval in radiation oncology; (4) Extend the techniques developed to create a multimodal image retrieval system in pathology