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中文摘要
翻译
描述(由申请人提供): 虽然商业计算机辅助检测(CAD)产品已被大量医疗机构用于辅助放射科医生筛查乳房X光检查,但由于相对较低的灵敏度和重复性以及较高的假阳性率,放射科医生往往对肿块的CAD提示结果缺乏信心,而与微钙化检测和表征的性能相比。对于CAD系统是否以及如何最好地帮助提高放射科医生的诊断性能,目前还没有达成一致意见。为了解决这些问题,我们建议开发和评估一个独特的交互式计算机辅助检测(ICAD)系统用于乳房X光检查。除了像目前的系统一样在处理后的图像上提供初始提示外,放射科医生还可以通过几种方式与ICAD系统进行交互。观察者可以选择乳房X光照片上的任何区域(线索或非线索),并查询ICAD。在收到观察者的请求后,ICAD将提取区域,计算特征向量,将该向量与参考库中的大量区域进行比较,并使用加权k近邻算法生成分类分数。此外,应要求,该系统将提供一个专门针对“最新的先前图像”进行优化的方案的结果,以使观察员能够评估可能出现的异常情况的“早期迹象”,以及后来(在随后的检查中)证明是恶性的异常情况。为了验证这一假设,本项目还将进行一项观察者性能研究,以比较六名放射科医生使用运行在领先商业产品和我们新提出的ICAD系统性能水平上的CAD系统时的性能差异。
英文摘要
DESCRIPTION (provided by applicant): Although commercial computer-aided detection (CAD) products have been used in a large number of medical institutions to assist radiologists in screening mammography, radiologists often have limited confidence in CAD cueing results for masses due to the relatively low sensitivity and reproducibility, as well as the higher false-positive rate, than that compared with the performance for microcalcification detection and characterization. There is no general agreement on whether and how a CAD system can best help improve radiologists' diagnostic performance. To address these issues, we propose to develop and evaluate a unique Interactive Computer-Aided Detection (ICAD) system for mammography. In addition to providing initial cues on the processed images as would current systems, radiologists can interact with the ICAD system in several ways. Observers can select any regions (cued or not cued) on the mammogram and query ICAD. Upon receiving a request from the observer, ICAD will extract the region, compute a feature vector, compare the vector with a large number of regions in a reference library, and generate a classification score using a weighted k-nearest neighbor algorithm. In addition, upon request, the system will provide the results of a scheme specifically optimized on "the latest prior images" to enable observers to assess "early signs" for abnormalities that may develop and later (on subsequent examinations) prove to be malignant. To test such a hypothesis, an observer performance study will also be carried out in this project to compare the performance difference when six radiologists use a CAD system operating at the performance level of leading commercial products and our newly proposed ICAD system.
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Administrative Core
  • 批准号:
    10334982
  • 项目类别:
  • 资助金额:
    $64.25万
  • 财政年份:
    2022
  • 负责人:
    Bin Zheng
  • 依托单位:
Oklahoma Center of Medical Imaging for Translational Cancer Research
  • 批准号:
    10334981
  • 项目类别:
  • 资助金额:
    $228.64万
  • 财政年份:
    2022
  • 负责人:
    Bin Zheng
  • 依托单位:
Regulation of interferon signaling in melanoma by the cohesin complex protein STAG2 via 3D genome organization
  • 批准号:
    10905899
  • 项目类别:
  • 资助金额:
    $37.44万
  • 财政年份:
    2022
  • 负责人:
    Bin Zheng
  • 依托单位:
Targeting the LKB1-AMPK pathway in melanoma: Mechanism and preclinical evaluation
  • 批准号:
    9690391
  • 项目类别:
  • 资助金额:
    $7.5万
  • 财政年份:
    2012
  • 负责人:
    Bin Zheng
  • 依托单位:
海外基金