开发成人脑瘤特征CNN:转移学习的含义和未来方向
Teesta Mukherjee1, Shramika Gour1, Saadullah Farooq Abbasi1
1Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
Studies in health technology and informatics
|August 23, 2024
概括
本研究使用卷积神经网络 (CNN) 和转移学习 (TL) 通过利用成人数据集来改善儿童的大脑瘤特征. 这种方法解决了儿科神经瘤学的数据局限性.
科学领域:
- 人工智能在医学中的应用
- 神经瘤学神经瘤学
- 医学图像分析 医学图像分析
背景情况:
- 大脑瘤是最常见的儿童固体瘤,但由于异质性和道德问题,数据稀缺性阻碍了研究.
- 由于有限的注释数据集,开发儿童脑瘤的有效表征模型具有挑战性.
研究的目的:
- 开发一个卷积神经网络 (CNN) 用于脑瘤的表征.
- 探索转移学习 (TL) 的应用,以适应在成人数据集上训练的模型与儿科脑瘤数据的应用.
- 解决神经瘤学中有限的注释儿科数据集的挑战.
主要方法:
- 使用成人BraTS 2020数据集进行初始卷积神经网络 (CNN) 模型培训.
- 雇员转移学习 (TL) 将知识从预训练成年模型转移到较小的数据集.
- 专注于从预训练模型中提取特征,以增强瘤特征.
主要成果:
- 证明了使用CNN用于脑瘤表征的可行性.
- 展示了转移学习 (TL) 的潜力,以克服儿科脑瘤研究中的数据限制.
- 建立了利用大型成人数据集用于儿科应用的方法.
结论:
- 拟议的转移学习 (TL) 方法为增强儿科脑瘤特征提供了一个有希望的解决方案.
- 这种方法可以显著改善对有限的儿科脑瘤数据集的分析.
- 这些发现对推进儿科神经瘤学和治疗策略有潜在的影响.
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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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