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Updated: Jul 17, 2026

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使用临床和成像数据进行乳腺癌风险分层的多模型机器学习框架.

Lu Miao1, Zidong Li2, Jinnan Gao1

  • 1Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.

Journal of X-ray science and technology
|February 20, 2025
PubMed
概括

这项研究开发了一个机器学习框架,整合了临床和深度学习成像功能,用于评估乳腺癌恶性病变. 基于堆叠的组合模型达到0.94的峰值AUC,显示出风险分层的高准确性.

关键词:
乳腺癌 乳腺癌 乳腺癌临床数据 临床数据深度学习是一种深度学习.图像数据 图像数据 图像数据机器学习是机器学习.堆叠组合合集 堆叠组合

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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