论文标题
MRI语义分割问题中知识转移的贝叶斯生成模型
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
论文作者
论文摘要
基于深度学习的自动分割方法最近证明了最先进的性能，表现优于普通方法。然而，这些方法对于小数据集不适用，这在医疗问题中很常见。为此，我们通过生成的贝叶斯先验网络提出了一种疾病之间的知识转移方法。将我们的方法与预训练方法和随机初始化进行了比较，并在DICE相似性系数指标上获得了脑瘤分割2018数据库（BRATS2018）的最佳结果。
Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods. Nevertheless, these methods are inapplicable for small datasets, which are very common in medical problems. To this end, we propose a knowledge transfer method between diseases via the Generative Bayesian Prior network. Our approach is compared to a pre-train approach and random initialization and obtains the best results in terms of Dice Similarity Coefficient metric for the small subsets of the Brain Tumor Segmentation 2018 database (BRATS2018).
