New Century Breast Cancer Diagnostics System

新世纪乳腺癌诊断系统

• 批准号: 7140098
• 负责人: SHIH-CHUNG B LO
• 金额: $ 19.36万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7140098
• 关键词: New; Century; Breast; Cancer; Diagnostics

DESCRIPTION (provided by applicant): In this project we hypothesize that a comprehensive clinical case library with intelligent agents can sort and render clinically similar cases and present clinically significant features, to assist the radiologist in interpreting mammograms. This computer library system is designed to integrate, search, and analyze clinical data that are analogous to the study case. In normal clinical practice, experienced radiologists often refer to their personal mental images of previous proven cases in making diagnoses and patient management decisions. Providing a computerized library can refresh a radiologist's mental memory with a broad array of proven cases and concrete visualizations. The system can also assist less experienced radiologists in making diagnoses by referring them not only to histologically proven cases but also to the statistical distribution of features. Freed from bias induced by their recent experience, radiologists can use such a library system to improve the diagnostic accuracy at large. This approach differs greatly from conventional computer-aided detection (CAD) or computer-aided diagnosis (CADx) methods. One can also expect that by adding the radiologist's guidance - for example BI-RADS descriptors - into the proposed mammographic library system will lead to increase the searching accuracy and achieve a greater diagnostic outcome. Technically speaking, this project differs from previous mammographic CADx and image-based retrieval methods in its emphasis on using local vector features and sector fuzzy features of the mammographic masses supported by our newly-invented maximum likelihood fuzzy shadow techniques and multiple circular path convolution neural network. In addition, the recent technical advances in image retrieval using the fuzzy feature matching method and hyper-space analysis techniques have made the success of the proposed approach highly possible. This computer-based library system can be extended to include other imaging modalities for breast care. It is conceivable that this comprehensive breast care database can further link local breast care centers through the coming high-speed network system. In addition, this system could also serve as a learning center for clinicians and scientists. This project represents a significant trend in the development of a comprehensive image data system for breast care in the next several decades.

描述（由申请人提供）： 在这个项目中，我们假设一个具有智能代理的综合临床病例库可以对临床相似的病例进行分类和呈现，并呈现临床上重要的特征，以协助放射科医生解释乳房X光检查。该计算机图书馆系统旨在集成、搜索和分析与研究案例类似的临床数据。在正常的临床实践中，经验丰富的放射科医生在做出诊断和患者管理决策时经常会参考之前已证实病例的个人心理图像。 提供计算机化图书馆可以通过大量经过验证的案例和具体的可视化来刷新放射科医生的心理记忆。该系统还可以帮助经验不足的放射科医生进行诊断，不仅参考组织学证实的病例，还参考特征的统计分布。放射科医生摆脱了近期经验带来的偏见，可以使用这样的库系统来提高整体诊断的准确性。这种方法与传统的计算机辅助检测（CAD）或计算机辅助诊断（CADx）方法有很大不同。人们还可以预期，通过将放射科医师的指导（例如 BI-RADS 描述符）添加到所提议的乳房 X 线摄影库系统中，将提高搜索准确性并实现更好的诊断结果。 从技术上讲，该项目与之前的乳腺 X 线摄影 CADx 和基于图像的检索方法的不同之处在于，它强调使用乳腺 X 线摄影肿块的局部矢量特征和扇区模糊特征，并由我们新发明的最大似然模糊阴影技术和多循环路径卷积神经网络支持。此外，最近使用模糊特征匹配方法和超空间分析技术的图像检索技术的进步使得所提出的方法的成功成为可能。 这个基于计算机的图书馆系统可以扩展到包括其他乳房护理成像模式。 可以想象，这个综合的乳腺保健数据库可以通过即将到来的高速网络系统进一步链接当地的乳腺保健中心。此外，该系统还可以作为临床医生和科学家的学习中心。该项目代表了未来几十年乳房护理综合图像数据系统发展的重要趋势。