English(EN) S$^3$F-Net: A Multi-Modal Approach to Medical Image Classification via Spatial-Spectral Summarizer Fusion Network

新的S3F-Net医学成像模型融合了空间和光谱数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种新的多模态医学图像分类网络S$^3$F-Net，它结合了空间和光谱特征学习。该双分支框架将用于空间特征的深度卷积神经网络与一种新颖的浅层光谱编码器SpectraNet相结合。SpectraNet使用一种直接在傅里叶频谱上操作的光谱滤波器层，实现了全局感受野，并比仅空间方法提高了性能，准确率提高了5.13%。该网络在BRISC2025数据集上达到了98.76%的具有竞争力的准确率，并展示了对不同病理的适应性。 AI

影响引入了一种新颖的医学图像分析融合技术，有可能提高诊断准确性和跨模态的泛化能力。

排序理由这是一篇描述用于医学图像分类的新型模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Md. Saiful Bari Siddiqui, Mohammed Imamul Hassan Bhuiyan · 2026-09-10 04:00

S$^3$F-Net：一种通过空间-光谱摘要器融合网络实现医学图像分类的多模态方法

arXiv:2509.23442v2 Announce Type: replace-cross Abstract: Convolutional Neural Networks have become a cornerstone of medical image analysis due to their proficiency in learning hierarchical spatial features. However, this focus on a single domain is inefficient at capturing globa…

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S$^3$F-Net：一种通过空间-光谱摘要器融合网络实现医学图像分类的多模态方法

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