ARTIFICIAL NEURAL NETWORKS IN MAMMOGRAPHY - APPLICATION TO DECISION-MAKING IN THE DIAGNOSIS OF BREAST-CANCER
ARTIFICIAL NEURAL NETWORKS IN MAMMOGRAPHY - APPLICATION TO DECISION-MAKING IN THE DIAGNOSIS OF BREAST-CANCER
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DOI：
10.1148/radiology.187.1.8451441
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发表时间：
1993-04-01
期刊：
影响因子：
19.7
通讯作者：
METZ, CE
中科院分区：
文献类型：
--
作者：
WU, YZ;GIGER, ML;METZ, CE
作者研究了人工神经网络在乳腺X线摄影数据分析中作为放射科医生决策辅助的潜在效用。三层，前馈神经网络与反向传播算法的基础上，由经验丰富的放射科医生从乳房X线照片中提取的特征，乳房X线照片的解释进行了训练。使用43个图像特征的网络在区分良性和恶性病变方面表现良好，在循环法测试中，教科书病例的受试者工作特征曲线下的面积值为0.95。在临床病例中，发现神经网络在合并14个放射科医生提取的病变特征以区分良性和恶性病变方面的性能高于单独主治和住院放射科医生的平均性能（没有神经网络的帮助）。作者得出结论，这种网络可能提供一个潜在的有用的工具，在乳腺摄影决策任务，区分良性和恶性病变。
The authors investigated the potential utility of artificial neural networks as a decision-making aid to radiologists in the analysis of mammographic data. Three-layer, feed-forward neural networks with a back-propagation algorithm were trained for the interpretation of mammograms on the basis of features extracted from mammograms by experienced radiologists. A network that used 43 image features performed well in distinguishing between benign and malignant lesions, yielding a value of 0.95 for the area under the receiver operating characteristic curve for textbook cases in a test with the round-robin method. With clinical cases, the performance of a neural network in merging 14 radiologist-extracted features of lesions to distinguish between benign and malignant lesions was found to be higher than the average performance of attending and resident radiologists alone (without the aid of a neural network). The authors conclude that such networks may provide a potentially useful tool in the mammographic decision-making task of distinguishing between benign and malignant lesions.