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MMSupcon:一种基于图像融合的多模式监督对比方法用于脑瘤诊断

Haoyu Wang¹, Jing Zhang², Siying Wu²

¹University of Science and Technology of China, 230000, Hefei, China; Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China.

Artificial intelligence in medicine

|September 7, 2025

概括

这项研究引入了一种用于脑瘤诊断的新型多模式监督对比学习方法 (MMSupcon). 通过融合MRI数据和增强特征学习,MMSupcon提高了准确性,优于现有方法.

科学领域:

医疗成像医学成像
人工智能的人工智能
机器学习机器学习

背景情况:

准确的脑瘤诊断对于有效治疗至关重要.
磁共振成像 (MRI) 是一种关键的非侵入性诊断工具.
整合多模态MRI数据对于瘤诊断中的模式识别至关重要,但因样本稀缺而受到限制.

研究的目的:

提出一种新的训练范式,即多模式监督对比学习 (MMSupcon),以应对有限的多模式成像样本在脑瘤诊断中的挑战.
通过有效地融合互补的MRI模式和优化特征学习来提高诊断准确性.

主要方法:

开发了一种多模式医疗图像融合组件,以创建信息丰富的样本.
引入了多模式监督对比损失以指导特征学习,保持跨模式完整性和模式的独特性.
在北京天丹医院的真实脑瘤数据集和两个公开的BraTS质瘤分类数据集上验证了MMSupcon方法.

主要成果:

在真实世界的数据集上,MMSupcon实现了最先进的性能.
在两个公开的BraTS质瘤分类数据集上显示了显著的性能改进.
拟议的方法有效地提高了多模式脑瘤成像中的诊断准确性.

结论:

在多模式脑瘤诊断方面,MMSupcon提供了显著的进步.
该方法成功地克服了稀缺的多模式成像样本的局限性.
这种方法有望提高临床诊断准确性和患者的治疗结果.

关键词:

大脑瘤的诊断脑瘤的诊断磁共振成像技术磁共振成像技术多模式图像融合多模式图像融合监督的对比学习学习

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