论文标题
揭示CTS的肺部感受。对处理体积数据的各种深度学习方法的比较分析
Revealing Lung Affections from CTs. A Comparative Analysis of Various Deep Learning Approaches for Dealing with Volumetric Data
论文作者
论文摘要
本文介绍并相对分析了几种在Imageclef 2020结核病任务的背景下，在肺CTS中自动检测与结核病相关的病变的几种深度学习方法。讨论和评估了三类方法，相对于对基于神经网络的分类器的输入的输入方式不同。所有这些都进行了丰富的实验分析，其中包括各种神经网络架构，各种分割算法和数据增强方案。报告的工作属于Senticlab.uaic团队，该团队在比赛中获得了最佳成绩。
The paper presents and comparatively analyses several deep learning approaches to automatically detect tuberculosis related lesions in lung CTs, in the context of the ImageClef 2020 Tuberculosis task. Three classes of methods, different with respect to the way the volumetric data is given as input to neural network-based classifiers are discussed and evaluated. All these come with a rich experimental analysis comprising a variety of neural network architectures, various segmentation algorithms and data augmentation schemes. The reported work belongs to the SenticLab.UAIC team, which obtained the best results in the competition.
