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在有限的数据场景中,专家级的儿科脑瘤细分与逐步转移学习的有限数据场景

Aidan Boyd^1,2, Zezhong Ye^1,2, Sanjay Prabhu³

¹Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA.

medRxiv : the preprint server for health sciences

|July 10, 2023

概括

人工智能 (AI) 模型实现了小儿低度质瘤的专家级自分类,证明了高临床可接受性. 这种逐步转移学习方法显示了人工智能在有限数据的儿科脑瘤细分领域的前景.

科学领域:

医疗成像医学成像
在瘤学中使用人工智能
儿科神经瘤学儿科神经瘤学

背景情况:

儿童质瘤的自动瘤划分对于实时体积评估和临床决策至关重要.
对于儿科瘤,现有的自细分算法受到数据可用性和临床翻译挑战的限制.

结论:

逐步转移学习使专家级自动化儿科脑瘤细分和体积测量具有高临床可接受性.
这种方法促进了人工智能成像细分算法的开发和翻译,即使是在数据有限的场景中.
人工智能驱动的自细分具有改善儿科质瘤诊断和治疗监测的巨大潜力.

关键词:

自动瘤细分系统自动瘤细分系统深度神经网络是一个神经网络.这就是为什么MRI是MRI.儿科低级质瘤儿科低级质瘤在联合国网络上.

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