论文标题
基于新兴语言的胸部CT体积中的Covid-19肺部感染的符号语义细分和解释
Symbolic Semantic Segmentation and Interpretation of COVID-19 Lung Infections in Chest CT volumes based on Emergent Languages
论文作者
论文摘要
冠状病毒疾病(Covid-19)导致大流行使对日常生活至关重要的服务的广度瘫痪。计算机断层扫描(CT)切片中肺部感染的分割可用于改善患者的诊断和理解。深度学习系统由于其黑匣子性质而缺乏可解释性。受到人类通过语言进行复杂思想的交流的启发,我们提出了一个基于新兴语言的象征框架,以分割肺CT扫描中的COVID-19感染。我们对两个人工代理之间的合作建模 - 发件人和一个接收器。这些代理使用新兴的符号语言协同合作,以解决语义分割的任务。我们的游戏理论方法是对代理之间的合作进行建模,这与生成的对抗网络(GAN)不同。发件人从深网的较高层之一中检索信息,并从词汇的分类分布中产生符号句子。接收器摄入符号流并进行分割面罩。开发了一种私人的紧急语言,该语言形成了用于描述Covid感染分割任务的通信渠道。我们使用符号发生器来增强现有的最新语义分割架构架构,以形成符号分割模型。我们的象征性分割框架实现了由COVID-19引起的肺部感染分割的最先进的表现。我们的结果显示了对符号句子的直接解释,以区分正常区域和受感染区域,感染形态和图像特征。我们显示了CT中COVID-19肺部感染分割的最先进的结果。
The coronavirus disease (COVID-19) has resulted in a pandemic crippling the a breadth of services critical to daily life. Segmentation of lung infections in computerized tomography (CT) slices could be be used to improve diagnosis and understanding of COVID-19 in patients. Deep learning systems lack interpretability because of their black box nature. Inspired by human communication of complex ideas through language, we propose a symbolic framework based on emergent languages for the segmentation of COVID-19 infections in CT scans of lungs. We model the cooperation between two artificial agents - a Sender and a Receiver. These agents synergistically cooperate using emergent symbolic language to solve the task of semantic segmentation. Our game theoretic approach is to model the cooperation between agents unlike Generative Adversarial Networks (GANs). The Sender retrieves information from one of the higher layers of the deep network and generates a symbolic sentence sampled from a categorical distribution of vocabularies. The Receiver ingests the stream of symbols and cogenerates the segmentation mask. A private emergent language is developed that forms the communication channel used to describe the task of segmentation of COVID infections. We augment existing state of the art semantic segmentation architectures with our symbolic generator to form symbolic segmentation models. Our symbolic segmentation framework achieves state of the art performance for segmentation of lung infections caused by COVID-19. Our results show direct interpretation of symbolic sentences to discriminate between normal and infected regions, infection morphology and image characteristics. We show state of the art results for segmentation of COVID-19 lung infections in CT.