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Updated: Jan 16, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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基于深度学习方法的结肠直肠多片细分:系统性审查

Xin Liu1, Nor Ashidi Mat Isa1, Chao Chen1,2

  • 1School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Pulau Pinang 14300, Malaysia.

Journal of imaging
|September 26, 2025
PubMed
概括
此摘要是机器生成的。

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本综述系统地分析了聚细分方法,这对于早期发现结肠直肠癌至关重要. 它涵盖了深度学习,Mamba和视频技术,评估了44个模型和数据集.

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 大肠直肠癌是全球领先的恶性瘤.
  • 早期检测和评估是预防癌症的关键.
  • 聚合物细分有助于有针对性的治疗计划.

研究的目的:

  • 系统地审查和分析多细分方法.
  • 提供深度学习,Mamba和视频细分技术的概述.
  • 评估模型性能并讨论聚合物细分的未来趋势.

主要方法:

  • 对146篇论文 (2018-2024) 的系统文献综述.
  • 分析深度学习,Mamba和基于视频的多片细分架构.
  • 使用标准指标对44个细分模型的性能评估.

主要成果:

  • 详细分析了多片细分技术的演变.
  • 关于当前的深度学习和基于Mamba的方法的全面概述.
  • 评估各种模型的细分性能和实时功能.

结论:

  • 息肉细分是一个快速发展的领域,具有重大临床影响.
关键词:
马姆巴·马姆巴是什么意思深度学习是一种深度学习.聚合物细分的聚合物细分

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  • 深度学习和像Mamba这样的新兴方法显示出提高准确性的承诺.
  • 需要进一步的研究来应对当前的挑战,并探索未来的趋势.