论文标题
基于新兴象征语言的深度医学图像分类
Emergent symbolic language based deep medical image classification
论文作者
论文摘要
用于医学图像分类的现代深度学习系统表明，可以区分基于图像的医疗类别的特殊功能。但是，他们的易害性严重阻碍了他们解释决策背后的推理。这部分是由于神经网络工作的不可解释的持续不断表示。最近已显示出来的语言（EL）通过在参考游戏框架中为它们提供符号代表，从而增强了神经网络的功能。符号表示是高度可解释的良好老式AI（GOFAI）系统的基石之一。在这项工作中，我们首次证明了在图像分类的框架中，新兴语言的深度符号表示。我们表明，基于EL的分类模型可以执行，甚至比最先进的深度学习模型的状态更好。此外，它们提供了符号表示，该表示打开了涉及符号操纵的可解释Gofai方法的整个可能性。我们使用CHEXPERT数据集证明了基于免疫细胞标记的细胞分类和胸部X射线分类的EL分类框架。代码可从https://github.com/arichow/el在线获得。
Modern deep learning systems for medical image classification have demonstrated exceptional capabilities for distinguishing between image based medical categories. However, they are severely hindered by their ina-bility to explain the reasoning behind their decision making. This is partly due to the uninterpretable continuous latent representations of neural net-works. Emergent languages (EL) have recently been shown to enhance the capabilities of neural networks by equipping them with symbolic represen-tations in the framework of referential games. Symbolic representations are one of the cornerstones of highly explainable good old fashioned AI (GOFAI) systems. In this work, we demonstrate for the first time, the emer-gence of deep symbolic representations of emergent language in the frame-work of image classification. We show that EL based classification models can perform as well as, if not better than state of the art deep learning mod-els. In addition, they provide a symbolic representation that opens up an entire field of possibilities of interpretable GOFAI methods involving symbol manipulation. We demonstrate the EL classification framework on immune cell marker based cell classification and chest X-ray classification using the CheXpert dataset. Code is available online at https://github.com/AriChow/EL.
