利用竞争神经网络研究人类乳腺疾病的显微图像

R. Allan, W. Kinsner
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引用次数: 10

摘要

竞争神经网络为客观地从医学图像中提取特征提供了独特的机会。这种方法的一个优点是医学图像分析可以自动化或半自动化。这种自动化可以提高诊断解释的精度和准确性,而半自动化可以实现大致相同的目标,并将作为完全自动化的自然垫脚石。本文表明,所有类型的竞争神经网络都可以从显微镜下获得的四种人类乳腺疾病(两种良性和两种恶性)的图像中提取出一般特征。定性地评估,这些特征广泛地包括阈值和边缘检测。无论是否有监督,都可以提取这些特征。肉眼检查,良恶性诊断没有明显的明显区别,这是组织诊断中最重要的区别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A study of microscopic images of human breast disease using competitive neural networks
Competitive neural networks offer a unique opportunity to extract features from medical images objectively. An advantage of this approach is that medical image analysis could be automated or semi-automated. This automation could lead to improved precision and accuracy of diagnostic interpretation, while semi-automation could achieve much the same goal and would serve as a natural stepping-stone to full automation. This paper shows that all types of competitive neural networks can extract general features from images obtained through a microscope of four types of human breast disease, two benign and two malignant. Assessed qualitatively, the features broadly encompass thresholding and edge detection. These features are extracted regardless of supervision or lack of supervision. To visual inspection, there are no obvious sharp distinctions between benign and malignant diagnoses, the most important distinction in tissue diagnosis.
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