Manifold Learning-based Data Sampling for Model Training
Manifold Learning-based Data Sampling for Model Training
复制标题
DOI：
10.1007/978-3-662-56537-7_70
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Shuqing Chen;Sabrina Dorn;M. Lell;M. Kachelriess;A. Maier
中科院分区：
文献类型：
--
作者：
Shuqing Chen;Sabrina Dorn;M. Lell;M. Kachelriess;A. Maier
训练数据采样是机器学习中的一项重要任务，特别是对于小样本数据和非均匀样本分布的数据。将数据随机划分为不同的数据集可能会导致训练模型只覆盖部分样本，并且对弱样本情况不准确。最近的研究表明，流形学习技术在医学图像处理中的好处。在这项工作中，我们提出了一种基于流形学习的方法来改善数据划分和模型训练。我们使用图谱配准框架和深度学习框架评估了所提出的方法。最后比较了有无数据平衡的分割结果。在将基于流形学习的方法实施到框架中之后，所有的最终分割都得到了改进。最大的改善是24.4%。因此，所提出的基于流形学习的方法是有效的模型训练。
Training data sampling is an important task in machine learning especially for data with small sample size and data with nonuniform sample distribution. Dividing data into different data sets randomly can cause the problem that, the training model covers only parts of the sampled cases and works inaccurately for weakly sampled cases. Recent research showed the benefit of manifold learning techniques in medical image processing. In this work, we propose a manifold learning based approach to improve the data division and the model training. We evaluated the proposed approach using an atlas registration framework and a deep learning framework. The final segmentation results using methods with and without data balancing were compared. All of the final segmentations were improved after implementing the manifold learning based approach into the frameworks. The largest improvement was 24.4%. Thus, the proposed manifold learning based approach is effective for the model training.