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A New Computerized Method for Missed Cancer Detection in Screening Mammography

机译:一种新的筛选乳腺癌癌症检测的新计算机化方法

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The development of computer-aided diagnosis (CAD) methods to improve a radiologist's decision-making in reading mammography is facing a bottle-neck problem. One critical reason is that almost all current CAD methods are actually developed to perform as an independent "reader" rather than a complementary assistant "second reader" to the radiologists. This paper proposes a new computer aided detection (CAD) method to improve early detection of breast cancer in screening mammograms by focusing on computerized analysis and detection of cancers missed by radiologists. The strategies taken in this study include (a) multi-mode detection by breast density classification, (b) breast area partition and region based adaptive detection, (c) weighted classification using the distinguishing features identified in missed cancer analysis. The results demonstrated that, due to the effective modification strategies taken in the new system, detection performance was improved significantly for mammograms at both detected and missed stages. With the focus on missed cancer analysis and detection, a bigger improvement was obtained in detecting missed cases even though the general detection performance is still lower than that at detected stage. It is also observed that the cancer could be detected earlier with less false positive signals by using the new CAD strategy.
机译:计算机辅助诊断(CAD)方法的发展改善放射科医师在阅读乳房X线摄影X线摄影中的决策正面临着瓶颈问题。一种关键原因是实际上几乎所有当前的CAD方法都开发成作为独立的“读者”而不是互补助理“第二读者”到放射科学家。本文提出了一种新的计算机辅助检测(CAD)的方法来提高在通过专注于计算机分析和由放射科医师错过的癌症的检测乳房X光检查早期检测乳腺癌。本研究采用的策略包括(a)通过乳房密度分类的多模检测,(b)乳房区域分区和基于区域的自适应检测,(c)使用错过的癌症分析中鉴定的区别特征进行加权分类。结果表明,由于新系统中采取的有效修饰策略,检测和错过阶段的乳房X锤显着改善了检测性能。随着遗漏癌症分析和检测的重点,即使一般检测性能仍然低于检测到的阶段,也可以获得更大的改善。还观察到,通过使用新的CAD策略,可以使用较少的假阳性信号检测到癌症。

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