III-CXT-Small: Collaborative Research: Structuring, Reasoning, and Querying in a Very Large Medical Image Database

图像数据在医学信息学中具有巨大的实际重要性，也是工业界和学术界研究人员非常感兴趣的主题。虽然数字图像数据库现在在临床和教育环境中很普遍，并且与这些集合进行交互和查询的传统方法可以提供某种程度的有用功能，但很少有系统试图弥合这两个方面的差距。语义鸿沟。本基金提出的工作是一项多机构合作，将医学图像处理、机器学习和模式识别、知识表示和查询以及该领域专家的评估研究结合起来，旨在推动这一方向的最新技术。由国家医学图书馆和国家癌症研究所收集的6万张cervigram图像档案是一个理想的收藏。NLM图像存档形成一个狭窄的图像域，具有有限的和可预测的可变性。在这种情况下，领域知识的显式表示减轻了场景的低级感官记录（原始图像数据）与图像中隐含的对象和过程（语义解释）之间的语义差距。本研究将遵循信息层次结构，从原始图像数据到低级图像特征，识别物体和组织类型，基于知识的疾病过程推理，最后是工具和可视化，以支持临床和NLM/NCI合作者的诊断决策。研究团队将采用一种被称为计算机辅助视觉交互识别（CAVIAR）的基础范式，该范式将领域专家视为方程式的一个组成部分，并试图优化整个人机系统的性能。在数据库规模和复杂性迅速增长的同时，图像内容理解仍然被认为是一个令人烦恼的开放性问题。预计这项工作将对与医学图像分析有关的领域产生积极影响，包括信息提取、组织、表示和查询，以及培训。通过对NLM/NCI宫颈影像档案的关注，本研究可能有助于推动宫颈造影作为一种比巴氏涂片检查和阴道镜检查更经济有效的筛查宫颈癌的方法的作用。这个目标领域项目的结果也可以阐明差距，并帮助在更广泛的领域（如多媒体内容结构、理解、索引和检索）建立新的研究优先级。

Image data is of immense practical importance in medical informatics, and a subject of strong interest to researchers in industry and academia. While digital image databases are now prevalent in clinical and educational settings, and traditional means for interacting with and querying such collections can provide some level of useful functionality, there are few examples of systems that attempt to bridge the ?semantic gap.? The work proposed in this grant is a multi-institutional collaboration combining research in medical image processing, machine learning and pattern recognition, knowledge representation and querying, and evaluation by domain experts in the field, is intended to advance the state-of-the-art in this direction. The archive of 60,000 cervigram images assembled by the National Library of Medicine and National Cancer Institute is an ideal collection for this purpose. The NLM cervigram archive forms a narrow image domain that has a limited and predictable variability. In such cases, explicit representation of domain knowledge alleviates the semantic gap between the low-level sensory recordings of a scene (raw image data), and objects and processes implied from images (semantic interpretation). This research will follow an information hierarchy that proceeds from raw image data to low-level image features, recognition of objects and tissue types, knowledge-based reasoning about disease processes, and, finally, tools and visualizations to support diagnosis decisions by clinical and NLM/NCI collaborators. The research team will employ an underlying paradigm known as Computer-Assisted Visual Interactive Recognition, or CAVIAR, which considers the domain expert an integral part of the equation and attempts to optimize the performance of the complete human-machine system. Intellectual Merit Image content understanding is still considered a vexing open problem at the same time databases are growing rapidly in size and complexity. It is anticipated that this work will have a positive impact in areas relating to medical image analysis, including information extraction, organization, representation, and querying, as well as in training. Broader Impact Through the focus on the NLM/NCI cervigram archive, this research may help advance the role of cervicography as a more cost-effective procedure than pap smears and colposcopy in screening for cervical cancer. Results from this targeted-domain project could also illuminate gaps and help establish new priorities for research in broader domains such as multimedia content structuring, understanding, indexing, and retrieval.