Patterns out of cases using Kohonen maps in breast cancer diagnosis
Patterns out of cases using Kohonen maps in breast cancer diagnosis
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DOI：
10.1142/s012906570800135x
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发表时间：
2008-02-01
影响因子：
8
通讯作者：
Vilasis, X.
中科院分区：
文献类型：
--
作者：
Fornells, A.;Martorell, J. M.;Vilasis, X.
DESMAI 是一个帮助专家进行乳腺癌诊断的框架。它允许专家在注意确定样本是良性还是恶性时，根据一定的拓扑标准探索数字乳腺X线图像数据库。通过这种方式，他们可以获得补充信息，以增强他们的解释和预测。该应用程序的核心是 SOMCBR 系统，它是基于案例的推理系统的变体。通过使用自组织映射来组织案例内存。本文提出了一种通过案例和集群之间的关系来提高 SOMCBR 可靠性的策略。该方法已成功应用于 DESMAI，用于估计（如果可能）恢复的乳房 X 线摄影的类别。
DESMAI is a framework for helping experts in breast cancer diagnosis. It allows experts to explore digital mammographic image databases according to a certain topology criteria when they heed to decide whether a sample is benign or malignant. In this way, they are provided with complementary information to enhance their interpretations and predictions. The core of the application is a SOMCBR system, which is variant of a Case-Based Reasoning system featured. by organizing the case memory using a Self-Organizing Map. The article presents a strategy for improving the SOMCBR reliability thanks to the relations between cases and clusters. The approach is successfully applied in DESMAI for estimating, if it is possible, the class of the recovered mammographies.