Clinical Image Retrieval: User needs assessment, toolbox development & evaluation

临床图像检索：用户需求评估、工具箱开发

• 批准号: 7739714
• 负责人: Jayashree Kalpathy-Cramer
• 金额: $ 10.5万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7739714
• 关键词: Accounting; Address; Affinity; Algorithms; Anatomy; Applications Grants; Archives; Area; Arts; Award; Back; Characteristics; Clinical; Communication; Communities; Computer software; Data; Development; Development Plans; Diagnosis; Diagnostic Imaging; Diffusion; Distance Learning; Education; Educational process of instructing; Effectiveness; Evaluation; Feedback; Goals; Gold; Growth; Head and Neck Cancer; Health Sciences; Healthcare; Hospitals; Image; Image retrieval system; Imaging technology; Information Retrieval; Institutes; Internet; Interview; Judgment; Learning; Libraries; Link; Lung; Machine Learning; Malignant Neoplasms; Maps; Measures; Medical; Medical Imaging; Medical Informatics; Medicine; Mentors; Mentorship; Methodology; Methods; Metric System; Modeling; Multimodal Imaging; National Cancer Institute; Natural Language Processing; Nature; Needs Assessment; Online Systems; Ontology; Oregon; Output; Participant; Pathology; Pathology Report; Pathway interactions; Patients; Performance; Play; Postdoctoral Fellow; Principal Investigator; Process; Property; Quality Control; Radiation; Radiation Oncology; Recruitment Activity; Reporting; Research; Research Personnel; Retrieval; Role; Semantics; Site; Staging; Structure; Students; Surveys; System; Techniques; Technology; Testing; Text; Training; United States National Institutes of Health; Universities; Visual; Vocabulary; Work; Writing; base; biomedical informatics; cancer site; care delivery; career development; data mining; digital imaging; experience; follow-up; image processing; improved; meetings; oncology; open source; satisfaction; skills; success; tool; treatment planning; visual information

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

描述（由申请人提供）： 近年来，数字成像技术的进步导致医院、医疗系统和互联网上创建和存储的数字图像数量大幅增长。有效的医学图像检索系统可以在教学、研究、诊断和治疗中发挥重要作用。历史上图像是使用基于文本的方法检索的。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。尽管最近取得了进展，但纯粹基于内容的图像检索技术在捕获用户查询的语义本质的能力方面远远落后于文本检索技术。初步研究表明，一种更有前途的方法是自适应地结合这些互补技术来满足用户及其信息需求。然而，为了使这些方法取得成功，研究人员除了全面了解相关临床领域外，还需要提高计算技能。此独立之路 (K99/R00) 拨款申请描述了一项培训和职业发展计划，该计划将使俄勒冈健康与科学大学医学信息学 NLM 博士后研究员能够实现这些目标。培训部分将在 W. Hersh 博士和 Gorman 博士（用户研究）的指导下进行。 Fuss 博士（放射医学）和 Erdogmus 博士（机器学习）在其专业领域提供额外指导。 该独立之路 (K99/R00) 项目的长期目标是通过更好地理解用户需求并提出可缩小语义差距的多模态图像检索自适应方法来改进视觉信息检索。奖励期间，活动将围绕以下具体目标展开：（1）了解放射肿瘤学新手和专家用户的图像检索需求，制定评估金标准； (2) 开发语义、多模态图像检索算法； (3) 对放射肿瘤学中的自适应图像检索进行基于用户的评估； (4) 扩展所开发的技术以创建病理学多模态图像检索系统