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首页> 外文会议>International Conference on Pattern Recognition Systems >Deep Convolutional Neural Network to predict 1p19q co-deletion and IDH1 mutation status from MRI in Low Grade Gliomas
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Deep Convolutional Neural Network to predict 1p19q co-deletion and IDH1 mutation status from MRI in Low Grade Gliomas

机译：深度卷积神经网络预测低级胶质瘤中MRI的1P19Q共缺失和IDH1突变状态

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摘要

Predicting arm-chromosomes 1p19q co-deletion and IDH1 mutation in Low Grade Gliomas is determinant in the treatment planning and follow up of the patients. This study aims at proposing a non-invasive method, based on multimodal MR images using convolutional neural networks. The proposed approach consists in several preprocessing steps and an Inception architecture. We present comparative results on a publicly available dataset. The proposed Inception v3 architecture obtain a F1-score of 91.38 ± 5.7% in the test set, when classifying images between 1p19q preserved and codeleted and 82.07% ± 12% when classifying images between with and without IDH1 mutation.
机译：预测臂 - 染色体1P19Q共缺失和低级胶质瘤中的IDH1突变是治疗计划中的决定因素和患者的跟进。本研究旨在提出基于使用卷积神经网络的多模式MR图像的非侵入性方法。所提出的方法包括多个预处理步骤和成立架构。我们在公开的数据集中呈现比较结果。当在1P19Q保存和编码之间的图像之间进行分类时，所提出的初始V3架构在测试集中获得了91.38±5.7％的F1分数，并且在与IDH1突变之间分类图像之间的图像之间的分类，82.07％±12％。

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