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相关概念视频

Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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联合机器学习用于皮肤损伤诊断:一种异步和加权的方法.

Muhammad Mateen Yaqoob1, Musleh Alsulami2, Muhammad Amir Khan1

  • 1Department of Computer Science, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan.

Diagnostics (Basel, Switzerland)
|June 10, 2023
PubMed
概括

本研究介绍了一种隐私意识的机器学习方法,用于使用联合学习和卷积神经网络 (CNN) 检测皮肤癌. 该方法提高了诊断准确度,同时最大限度地减少了医疗保健中的数据隐私风险.

关键词:
分布式机器学习 (DLM) 是一种分布式的机器学习.为皮肤病变进行联合学习.隐私意识 机器学习 机器学习医疗保健中的隐私隐私意识的图像处理皮肤癌的预测 皮肤癌的预测

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科学领域:

  • 人工智能在医学中的应用
  • 计算皮肤病学 计算皮肤病学
  • 机器学习用于医疗保健

背景情况:

  • 准确和及时的皮肤癌诊断对于患者的治疗结果至关重要.
  • 医疗保健的传统机器学习面临着重大的数据隐私挑战.
  • 现有的方法可能无法充分平衡诊断准确性与隐私保护.

研究的目的:

  • 提出一种隐私意识的机器学习方法,用于增强皮肤癌检测.
  • 在医疗保健环境中应用机器学习时解决数据隐私问题.
  • 提高医疗诊断的联合学习模型的效率和准确性.

主要方法:

  • 利用异步联合学习与卷积神经网络 (CNN) 相结合.
  • 通过将CNN层分为浅层和深层组件来优化通信轮回.
  • 实施了一个时间加权的聚合策略,以改善中央模型的融合.

主要成果:

  • 与现有方法相比,提出的隐私意识的方法显示出更高的准确性.
  • 实现了更高的诊断准确性,并大大减少了通讯轮次.
  • 在整体通信成本效益方面,其表现优于当前技术.

结论:

  • 开发的方法为改善皮肤癌诊断提供了一个有希望的解决方案.
  • 在基于机器学习的医疗保健应用程序中有效解决关键数据隐私问题.
  • 具有时间权重的联合学习为保护隐私的医疗AI提供了一个可行的策略.