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

基本信息

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
项目类别：
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

项目摘要

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了在医院，医疗系统和互联网上创建和存储的数字图像数量的大幅增长。有效的医疗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。历史上使用基于文本的方法检索了图像。与图像相关的注释质量可以降低基于文本的图像检索的有效性。尽管最近进步，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显着落后于其文本对应。初步研究表明，一种更有希望的方法是适应性地结合这些互补技术，以适应用户及其信息需求。但是，对于这些成功的方法，研究人员除了获得对相关临床领域的全面了解外，还需要提高她的计算技能。这种独立途径（K99/R00）赠款申请描述了一项培训和职业发展计划，该计划将允许俄勒冈州健康与科学大学医学信息学博士后候选人候选人实现这些目标。培训组件将在W. Hersh博士的指导下与Gorman博士（用户研究）一起进行。 Fuss博士（放射医学）和Erdogmus博士（机器学习）在其专业领域提供了其他指导。这种独立途径（K99/R00）项目的长期目标是通过更好地了解用户需求并提出适应性方法来改善视觉信息检索，并为多模式图像检索提出自适应方法，以缩小语义差距。在奖励期间，活动将集中在以下具体目标上：（1）了解新手和放射肿瘤学专家的图像检索需求，并制定金标准以进行评估；（2）开发用于语义，多模式图像检索的算法；（3）对放射肿瘤学中自适应图像检索进行基于用户的评估；（4）扩展了为创建病理学多模式图像检索系统而开发的技术