KNOWLEDGE BASED SYSTEMS FOR DIAGNOSTIC HISTOPATHOLOGY

用于诊断组织病理学的基于知识的系统

基本信息

批准号：
2007904
负责人：
PETER H. BARTELS
金额：
$ 50.59万
依托单位：
UNIVERSITY OF ARIZONA
依托单位国家：
美国
项目类别：
财政年份：
1991
资助国家：
美国
起止时间：
1991-03-01 至 1997-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/2007904
关键词：
artificial intelligence computer assisted diagnosis computer system design /evaluation diagnosis design /evaluation diagnosis quality /standard histopathology information system analysis prognosis

项目摘要

We propose to develop a machine-vision system for the diagnostic interpretation of histopathologic sections and cytologic preparations. in continuation of an ongoing research project grant. The system will have "image understanding capability." that is, it will follow in its reasoning a model of the histology of a given application. The interpretive expert-system module will integrate concepts from human diagnostic knowledge with machine-computable histometric features. Systems of this kind can provide objective. quantitative, consistent evaluation of lesions, yielding more reliable interpretation and diagnostic, as well as prognostic, assessment. Using a combination of large data bases of digitized imagery for given diagnostic situations. and relational data bases (including patient history. treatment, and outcome), it is hoped that the objective assessment eventually will be related reliably to truth in diagnosis. The requirements for representative sampling are in the multimegapixel range, and require fully automatic scene segmentation and histometric feature extraction. This difficult problem has been largely resolved through ongoing support, employing an AI-based, adaptive segmentation approach. The major challenges for the proposed research are: 1) to gain an understanding of the necessary and sufficient human diagnostic clues and corresponding histometric analogs, that is, to determine the library of transforms to be used by the interpretive expert-system module; 2) to develop learning capability of the system for conceptual data; and 3) to gain an understanding of the functional requirements. dependence structure, and decision control capabilities of such a system.

我们建议开发一种用于诊断的机器视觉系统组织病理学切片和细胞学制剂的解释。在继续进行正在进行的研究项目拨款。系统会有 “图像理解能力。”也就是说，它将遵循其推理给定应用的组织学模型。解释性的专家系统模块将整合人类诊断的概念具有机器计算的组织特征的知识。这个系统种类可以提供目标。对病变进行定量、一致的评估，产生更可靠的解释和诊断，以及预测、评估。结合使用大型数据库针对给定诊断情况的数字化图像。和关系数据基础（包括患者病史、治疗和结果），希望客观评估最终将与事实可靠地联系起来诊断。代表性采样的要求是百万像素范围，并且需要全自动场景分割和组织统计特征提取。这个难题已经基本解决通过持续的支持，采用基于人工智能的自适应细分方法。拟议研究的主要挑战是：1）获得了解必要且充分的人类诊断线索以及相应的组织计量类似物，即确定的库转换为由解释专家系统模块使用； 2）到开发系统对概念数据的学习能力； 3) 至了解功能需求。依赖结构，以及此类系统的决策控制能力。