id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-164374-d3zojh1g	Chowdhury, Aritra	Symbolic Semantic Segmentation and Interpretation of COVID-19 Lung Infections in Chest CT volumes based on Emergent Languages	2020-08-22		.txt	text/plain	4582	347	52	title: Symbolic Semantic Segmentation and Interpretation of COVID-19 Lung Infections in Chest CT volumes based on Emergent Languages 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 propose a symbolic, game theoretic approach based on emergent languages to understand segmentation outputs in the context of lung infections in chest CT scans. We show, how we can significantly improve the performance of deep learning based segmentation networks by incorporating a symbolic layer that generates emergent language sentences. In this section, we detail relevant work in the area of segmentation of CT, medical image analysis of COVID-19 data, Emergent Languages and model interpretability in convolutional neural networks (CNNs) Therefore, we consider our symbolic semantic segmentation framework to provide a different paradigm of deep learning based segmentation, where we use the emergent symbolic language to understand and interpret the models with respect to the inputs and outputs.	./cache/cord-164374-d3zojh1g.txt	./txt/cord-164374-d3zojh1g.txt