跨物种数据集成用于病理学中增强层分化的跨物种数据集成
Junchao Zhu1, Mengmeng Yin2, Ruining Deng1
1Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
概括
使用小鼠脏数据可以改进人工智能模型对人类脏层进行细分. 这种跨物种的方法提高了准确性和概括性,特别是当人类数据有限时.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 腎臟病學 (nephrology) 是一種醫學專業.
背景情况:
- 精确的层细分对于诊断脏疾病至关重要.
- 深度学习模型需要大量的注释数据,由于隐私和稀缺性,很难获得这些数据.
- 外部数据集可能会引入噪声,阻碍模型的概括.
研究的目的:
- 调查使用跨物种同类数据 (老鼠脏) 的有效性,以改进人类脏层细分的深度学习模型.
- 为应对AI模型训练有限的注释临床数据的挑战.
主要方法:
- 联合训练的卷积神经网络 (CNN) 和基于变压器的语义细分模型,使用人类和小鼠脏数据集.
- 使用了PAS染色小鼠脏数据,因为其结构和特征与人类脏相似.
主要成果:
- 结合小鼠脏数据,导致皮质的平均交叉点在欧盟 (mIoU) 的平均增加为1.77%,髓的平均增加为1.24%.
- 子得分在皮质方面提高了1.76%,在脑髓方面提高了0.89%.
- 这种方法增强了细分模型的概括能力.
结论:
- 跨物种同类数据作为一个有价值的,低噪音的培训资源,用于提高人工智能模型在层细分中的性能.
- 这种方法在临床样本有限的情况下特别有效,为增强诊断工具提供了实际解决方案.
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