用深度学习进行医学图像翻译:进展,数据集和观点
Junxin Chen1, Zhiheng Ye1, Renlong Zhang2
1School of Software, Dalian University of Technology, Dalian 116621, China.
Medical image analysis
|May 1, 2025
概括
这篇评论探讨了基于深度学习的医疗图像翻译,这种方法可以准确地在模式之间转换图像. 它强调了进步,模型和应用,为这个领域的未来研究提供了洞察力.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 传统的医学图像生成常常省略患者特定数据,限制了临床使用.
- 医疗图像翻译准确地在模式之间转换图像,保留解剖学和跨模式特征.
- 这种技术为模型开发和临床实践提供了优势.
研究的目的:
- 审查基于深度学习的医学图像翻译的最新进展.
- 详细阐述该领域使用的各种任务,应用和基本模型.
- 确定未来的趋势,挑战和研究方向.
主要方法:
- 基本模型的概述:卷积神经网络 (CNN),变压器和状态空间模型 (SSM).
- 探索生成模型:生成对抗网络 (GAN),变异自编码器 (VAE),自行回归模型 (AR),扩散模型和流量模型.
- 讨论评估指标和用于评估翻译质量的常用数据集.
主要成果:
- 详细审查应用到医疗图像翻译的深度学习技术.
- 分析各种生成模型及其适用于不同翻译任务的适用性.
- 确定关键的评估指标和数据集,这些指标和数据集对于该领域的进展至关重要.
结论:
- 基于深度学习的医学图像翻译显示了临床应用的重大前景.
- 对先进模型,评估指标和数据集的持续研究至关重要.
- 本综述是研究人员推动医疗图像翻译创新的参考.
相关概念视频
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
02:07Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...