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SurvNet:一种低复杂度的卷积神经网络,用于对质母细胞瘤患者的生存时间进行分类

Qiyuan Lyu¹, Mario Parreno-Centeno², João Paulo Papa³

¹Electrical and Electronic Engineering Department, The University of Manchester, Oxford Rd, Manchester M13 9PL, United Kingdom.

|July 11, 2024

概括

使用磁共振成像 (MRI) 的深度学习模型SurvNet准确地预测了脑瘤存活率. 结合细分数据和四种MRI模式,它实现了82.4%的准确性,优于其他模型.

科学领域:

神经瘤学神经瘤学
医疗成像医学成像
人工智能的人工智能

背景情况:

恶性原发性脑瘤,特别是4级质瘤,预后不好,存活时间有限.
准确预测整体生存时间对于临床应用至关重要.
磁共振成像 (MRI) 的自动分析为脑瘤预后提供了洞察力.

研究的目的:

开发SurvNet,这是一个深度学习模型,用于将脑瘤患者的存活率分为长期和短期队列.
通过使用多种MRI模式和细分瘤数据来评估SurvNet的性能.
将SurvNet的预测准确度与已建立的深度学习模型进行比较.

主要方法:

提出了SurvNet,一个低复杂度的卷积神经网络架构.
整合了各种MRI模式 (例如T1) 作为增强特征提取的输入.
我们将SurvNet与手术前MRI数据集中的Inception V3,VGG 16和ensemble CNN模型进行了比较.
分析了细分脑瘤和训练数据对系统性能的影响.

主要成果:

使用T1MRI的SurvNet实现了62.7%的精度,超过了Inception V3 (52.9%),VGG 16 (58.5%) 和合奏CNN (54.9%).
增加MRI模式使SurvNet的准确性提高到76.5%,使用四种模式.
将细分数据和四种MRI模式结合起来,结果是最高准确率为82.4%.

结论:

与其他模型相比,SurvNet在分类脑瘤存活率方面表现出卓越的表现.
多参数MRI模式增强了SurvNet学习图像特征的能力,并提高了分类准确性.
完整的情景 (细分数据和四种MRI模式) 产生了最好的准确性,验证了细分在生存预测中的实用性.

关键词:

大脑瘤质瘤质母细胞瘤磁共振成像卷积神经网络卷积神经网络整体生存分类深度学习

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