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Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
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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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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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基于深度学习和可解释的人工智能的乳腺癌检测方案

Sandeep Saharan1, Niyaz Ahmad Wani2, Shreeya Chatterji2

  • 1Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India. sandeepsaharan@outlook.com.

Scientific reports
|September 1, 2025
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概括

通过结合卷积神经网络 (CNN) 和随机森林 (RF) 模型,DXAIB系统使用人工智能 (AI) 准确检测乳腺癌. 它通过SHAP等可解释人工智能 (XAI) 方法增强了信任,提供了清晰的诊断推理.

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科学领域:

  • 医学成像和诊断
  • 医疗保健中的人工智能
  • 机器学习用于疾病检测

背景情况:

  • 人工智能在医疗保健领域具有变革潜力, 但它的"黑子"性质阻碍了信任和采用.
  • 深度学习模型表现出高性能,但通常缺乏决策过程的透明度.
  • 对人工智能解释性的怀疑限制了其在临床环境中的实际应用.

研究的目的:

  • 推出DXAIB,一种用于精确检测乳腺癌的新型混合人工智能方案.
  • 在医疗诊断中解决人工智能解释性的关键挑战.
  • 增强人工智能驱动的医疗决策的透明度和信心.

主要方法:

  • 一种混合方法,将卷积神经网络 (CNN) 集成为特征学习和随机森林 (RF) 进行分类.
  • 实施DXAIB方案,用于自动化特征提取的卷积层.
  • 利用夏普利增量解释 (SHAP) 来实现人工智能预测的本地和全球解释性.

主要成果:

  • 与现有最先进的方法相比,DXAIB计划取得了更好的预测结果.
  • 通过混合CNN-RF方法进行有效的乳腺癌检测.
  • 提供了使用SHAP进行人工智能驱动的诊断预测的全面,特定水平的解释.

结论:

  • DXAIB为准确和可解释的乳腺癌检测提供了一个有前途的解决方案.
  • 整合SHAP显著提高了AI在医疗诊断中的透明度和可信度.
  • DXAIB 代表了可解释性人工智能 (XAI) 在医疗应用中的重大进步.