一个全集医疗图像对图像翻译系统
Luyi Han1, Tao Tan2, Yunzhi Huang3
1Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, 6525 GA Nijmegen, the Netherlands; Department of Radiology, Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands.
Cell reports methods
|August 12, 2025
概括
我们介绍了EVA-I2I模型用于医学图像翻译,利用DICOM标签信息的对比性语言图像预训练 (DCLIP). 这种方法可以同时对不同数据集进行培训,以增强域调整和零射击能力.
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:
- 公共的多域医学图像数据集越来越多,这有助于开发多功能图像对图像 (I2I) 翻译模型.
- 将各种成像协议集成到I2I模型中,在域编码和可扩展性方面提出了挑战.
研究的目的:
- 提出一种新的I2I翻译模型,EVA-I2I,能够同时处理多个领域.
- 为了利用DICOM标签信息的对比性语言图像预训 (DCLIP) 来改进域名表示.
主要方法:
- 使用七个公共数据集开发了EVA-I2I模型,其中包括27,950个脑,乳房,腹部和骨盆成像3D扫描.
- 利用DCLIP将自然语言扫描描述映射到共享的潜在空间中,增强了超越传统方法的表示.
主要成果:
- EVA-I2I模型成功地在单个会话中以高图像质量合成了所有训练的域.
- 在各种I2I翻译任务中表现出色,包括注册,分类和细分.
结论:
- 通过EVA-I2I,可以对各种医学成像数据集进行高效的训练,从而实现高质量的图像合成.
- 该模型可以在没有微调的情况下直接应用于外部数据集上的域调整,并显示了零射击域调整的潜力.
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