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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

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相关实验视频

Updated: Apr 30, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

使用模型投影进行联合学习,用于使用非IID数据进行多中心疾病诊断.

Jie Du¹, Wei Li¹, Peng Liu²

¹National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, Guangdong, China.

Neural networks : the official journal of the International Neural Network Society

|June 1, 2024

概括

使用模型投影 (FedMoP) 的联合学习通过防止模型遗忘和改进聚合来增强多中心疾病诊断. 与现有的联合学习技术相比,这种保护隐私的方法实现了更高的准确性和更快的融合.

关键词:

灾难性的遗忘.联合学习是联合学习.不有效的汇总.多中心疾病诊断多中心疾病诊断非IID数据的数据

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相关实验视频

Last Updated: Apr 30, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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科学领域:

人工智能的人工智能
机器学习机器学习
医疗信息学医疗信息学

背景情况:

多中心疾病诊断需要一个全球模型,但面临着隐私障碍与集中学习.
联合学习 (FL) 能够实现协作模式培训,同时保持当地的患者数据隐私.
在FL中,非独立且相同分布的 (非IID) 数据会导致灾难性的遗忘和缓慢的融合.

研究的目的:

解决灾难性遗忘和无效聚合在多中心疾病诊断的联合学习中的挑战.
提出一种创新的联合学习方法,使用模型投影 (FedMoP) 进行联合学习,以提高模型性能和融合.

主要方法:

采用模型投影 (FedMoP) 的联合学习被引入,以确保本地模型的性能在本地训练后不会退化.
FedMoP保证全球模型在局部数据上的表现在聚合后会有所改善,从而增强了趋同.
在关键培训阶段,该方法在没有直接访问全球或本地数据的情况下运行.

主要成果:

在准确性,收率和通信成本方面,FedMoP显著优于最先进的FL方法.
实验结果表明,FedMoP的准确性与集中式学习相当或超过.
拟议的方法有效地减轻了非IID联合学习中固有的灾难性遗忘和无效聚合问题.

结论:

FedMoP提供了一种保护隐私的解决方案,用于使用联合学习进行多中心疾病诊断.
这种方法提高了模型的准确性,融合速度,并减少了通信开销.
FedMoP为集中式学习提供了一个可行的替代方案,提供了优越或同等的性能与增强的隐私.