Wong Jocelyn
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:699-702. doi: 10.1109/EMBC.2015.7318458.
This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, with some combination operations between multiphase MRIs. Compared to other researches, which involve additional effort to design and choose feature sets, the approach used in this paper leverages the learning capability of deep learning machine. We present the grading performance on the testing data measured by the sensitivity and specificity. The results show a maximum improvement of 18% on grading performance of Convolutional Neural Networks based on sensitivity and specificity compared to Neural Networks. We also visualize the kernels trained in different layers and display some self-learned features obtained from Convolutional Neural Networks.
本文研究利用多期磁共振成像（MRI）图像进行脑肿瘤分级，并将结果与深度学习结构和基线神经网络的各种配置进行比较。MRI图像直接输入学习机器，并在多期MRI之间进行一些组合操作。与其他需要额外努力来设计和选择特征集的研究相比，本文采用的方法利用了深度学习机器的学习能力。我们展示了在测试数据上以灵敏度和特异性衡量的分级性能。结果表明，与神经网络相比，基于灵敏度和特异性的卷积神经网络分级性能最大提高了18%。我们还可视化了在不同层训练的内核，并展示了从卷积神经网络获得的一些自学习特征。
