医学图像分析的持续学习:对最近的进展和未来前景的全面审查
Pratibha Kumari1, Joohi Chauhan2, Afshin Bozorgpour1
1Faculty of Informatics and Data Science, University of Regensburg, Regensburg 93053, Germany.
Medical image analysis
|August 2, 2025
概括
持续学习使人工智能模型能够适应新的医疗数据,而无需昂贵的再培训,克服性能下降和隐私问题. 这项调查绘制了医学图像分析中不断变化的持续学习格局.
科学领域:
- 医学图像分析 医学图像分析
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 医学成像中的深度学习模型实现了高性能,但随着新的数据而降低.
- 用新数据重新训练模型在计算上昂贵,并引发隐私问题.
- 持续学习为模型提供了一个可持续的解决方案,使其适应不断变化的数据.
研究的目的:
- 提供医疗图像分析中持续学习技术的全面概述和分类.
- 澄清当前的研究格局,并确定关键挑战和未来方向.
- 为满足医疗领域的独特需求,例如数据注释成本和时间漂移.
主要方法:
- 对医疗图像分析中持续学习现有研究的系统审查.
- 持续学习技术的分类 (排练,规范化,架构,混合).
- 评估技术在放射学和组织病理学等子领域的适用性.
主要成果:
- 确定了关键挑战:灾难性遗忘,数据漂移,稳定性-可塑性权衡.
- 讨论了持续学习框架,场景,评估方案和指标.
- 分析了医疗专业中不同持续学习策略的受欢迎程度和适用性.
结论:
- 持续学习对于在动态的医疗环境中开发适应性AI至关重要.
- 解决医疗特异性挑战,如数据稀缺和注释,对于有效的持续学习至关重要.
- 未来的研究应该专注于强大的基准测试和新的技术,以实现持续的绩效.
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