从未配对的乳腺样本制作的人工乳腺癌MRI数据集
Aarthi Muthukumar1, Isain Zapata2, Amanda Brooks3
1Rocky Vista University, College of Osteopathic Medicine, Ivins, UT, USA.
Data in brief
|January 21, 2026
概括
这项研究引入了使用CycleGAN生成的人造乳腺MRI,解决了乳腺癌诊断中对配乳腺扫描-MRI数据集的缺乏问题. 这些合成数据增强了机器学习模型开发和隐私,同时降低了成本.
科学领域:
- 医疗成像医学成像
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 由于有限的配对数据集,医疗成像的机器学习面临挑战,特别是乳腺癌诊断.
- 乳房影像和MRI提供了互补的信息,但配对的数据集很少.
研究的目的:
- 使用CycleGAN生成大量人工乳腺癌MRI数据集.
- 为了解决机器学习的开源配对乳腺造影-MRI数据集的差距.
- 促进开发更具普遍性的诊断和治疗规划模型.
主要方法:
- 使用CycleGAN架构,训练在未配对的乳房显微镜和乳房MRI数据上.
- 采用对抗式学习来实现跨模式的翻译,而无需直接的图像对应.
- 生成合成乳腺癌MRI数据用于增强.
主要成果:
- 成功创建了一个人工乳腺癌MRI数据集.
- 证明了增强患者隐私和降低MRI采购成本的潜力.
- 使用现有的瘤检测模型评估合成MRI数据集的可靠性.
结论:
- 生成的人工MRI数据集支持开发针对乳腺癌的强大的机器学习模型.
- 通过交叉模式翻译进行合成数据增强为数据稀缺提供了可行的解决方案.
- 这种方法推进了瘤学中的多模式成像,并有助于资源有限的环境.
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