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微分神经架构 寻找医学图像细分:系统性审查和现场审计
Emil Benedykciuk1, Marcin Denkowski1, Grzegorz M Wójcik1
1Institute of Computer Science and Mathematics, Maria Curie Sklodowska University, Akademicka 9, Lublin, 20-033, Poland.
概括
微分神经架构搜索 (DNAS) 对医学图像细分有希望,但面临着挑战. 一项系统性审查发现有限的外部验证,代码释放和关注搜索稳定性,阻碍了临床使用.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 计算病理学计算病理学
背景情况:
- 医疗图像细分对于临床应用至关重要,但面临着像体积数据和域移动这样的独特挑战.
- 传统的神经架构搜索 (NAS) 对于3D医疗图像分割而言,计算成本昂贵.
- 可差分NAS (DNAS) 通过在共享超级网络中使用基于梯度的优化提供了更可行的方法.
研究的目的:
- 在医疗图像细分中系统地审查可差异化NAS (DNAS) 的应用.
- 分析现有DNAS研究的方法,报告标准和局限性.
- 建议改善DNAS用于医疗细分的透明度,可比性和临床可重复性.
主要方法:
- 基于PRISMA的系统文献审查,从2018年到2025年选多个数据库.
- 33篇论文代表了31种独特的DNAS方法用于医疗图像细分的数量分析.
- 审查的重点是外部验证,代码可用性,搜索稳定性和多目标优化等方面.
主要成果:
- 独立数据集的外部验证仅在~10%的研究中报告.
- 完整的代码发布,包括搜索实施,仅限于~26%的审查论文.
- 对搜索稳定性和多目标优化 (延迟,内存) 的实质性调查不常见 (每项约23%).
结论:
- 虽然DNAS对自动化医疗图像细分模型设计充满希望,但目前的做法在验证,可重复性和报告方面存在重大差距.
- 需要标准化的报告和方法来提高DNAS衍生模型的临床部署性.
- 拟议的NAS报告卡旨在提高医疗细分研究的透明度和可比性.
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