markdownabstract__Abstract__\ud\udPerformance of automated tissue classification in medical imaging depends on the choice of descriptive features. In this paper, we show how restricted Boltzmann machines (RBMs) can be used to learn features that are especially suited for texture-based tissue classification. We introduce the convolutional classification RBM, a combination of the existing convolutional RBM and classification RBM, and use it for discriminative feature learning. We evaluate the classification accuracy of convolutional and non-convolutional classification RBMs on two lung CT problems. We find that RBM-learned features outperform conventional RBM-based feature learning, which is unsupervised and uses only a generative learning objective, as well as often-used filter banks. We show that a mixture of generative and discriminative learning can produce filters that give a higher classification accuracy.
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机译：markdownabstract__Abstract __ \ ud \ ud在医学成像中自动组织分类的性能取决于描述性特征的选择。在本文中，我们展示了如何使用受限的Boltzmann机器（RBM）来学习特别适合基于纹理的组织分类的特征。我们介绍了卷积分类RBM，它是现有卷积RBM和分类RBM的组合，并用于区分特征学习。我们在两个肺部CT问题上评估了卷积和非卷积分类RBM的分类准确性。我们发现，RBM学习的特征优于传统的基于RBM的特征学习，后者是无监督的，仅使用生成性学习目标以及常用的滤波器组。我们表明，生成性学习和区分性学习的混合可以产生能够提供更高分类精度的过滤器。
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