arXiv 2510.151382025-10-21cs.CV

面向全切片图像分类的傅里叶变换多示例学习

Fourier Transform Multiple Instance Learning for Whole Slide Image Classification

Institute of Artificial Intelligence (IAI)（人工智能研究所）；College of Medicine, University of Central Florida（中央佛罗里达大学医学院）；Department of Computer Science, University of Central Florida（中央佛罗里达大学计算机科学系）

Anthony Bilic, Guangyu Sun, Ming Li, Md Sanzid Bin Hossain, Yu Tian, Wei Zhang, Laura Brattain, Dexter Hadley, Chen Chen

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AI总结：

针对全切片图像分类中现有多示例学习方法难捕捉全局依赖的问题，提出FFT-MIL框架，通过频域分支提取全局特征并与空间特征融合，在多个数据集和基准方法上稳定提升分类性能。

摘要：

全切片图像（Whole Slide Image, WSI）分类依赖带有空间补丁特征的多示例学习（Multiple Instance Learning, MIL），但由于WSI尺寸极大且补丁嵌入具有局部特性，现有方法难以捕捉全局依赖关系。这一局限阻碍了对鲁棒诊断预测至关重要的粗粒度结构的建模。我们提出傅里叶变换多示例学习（FFT-MIL），这是一个通过频域分支增强MIL以提供紧凑全局上下文的框架。该框架通过快速傅里叶变换从WSI中提取低频裁剪块，并经由由卷积层和最小-最大归一化组成的模块化FFT-Block进行处理，以缓解频率数据的高方差问题。学习到的全局频率特征通过轻量级集成策略与空间补丁特征融合，能够兼容多种MIL架构。我们在三个公开数据集（BRACS、LUAD和IMP）上，针对六种最先进的MIL方法评估了FFT-MIL。集成FFT-Block后，宏F1分数平均提升了3.51%，AUC提升了1.51%，在不同架构和数据集上均展现出一致的性能增益。这些结果表明，频域学习是捕捉WSI分类中全局依赖关系的有效且高效的机制，能够补充空间特征，并提升基于MIL的计算病理学的可扩展性与准确性。

英文摘要：

Whole Slide Image (WSI) classification relies on Multiple Instance Learning (MIL) with spatial patch features, yet existing methods struggle to capture global dependencies due to the immense size of WSIs and the local nature of patch embeddings. This limitation hinders the modeling of coarse structures essential for robust diagnostic prediction. We propose Fourier Transform Multiple Instance Learning (FFT-MIL), a framework that augments MIL with a frequency-domain branch to provide compact global context. Low-frequency crops are extracted from WSIs via the Fast Fourier Transform and processed through a modular FFT-Block composed of convolutional layers and Min-Max normalization to mitigate the high variance of frequency data. The learned global frequency feature is fused with spatial patch features through lightweight integration strategies, enabling compatibility with diverse MIL architectures. FFT-MIL was evaluated across six state-of-the-art MIL methods on three public datasets (BRACS, LUAD, and IMP). Integration of the FFT-Block improved macro F1 scores by an average of 3.51% and AUC by 1.51%, demonstrating consistent gains across architectures and datasets. These results establish frequency-domain learning as an effective and efficient mechanism for capturing global dependencies in WSI classification, complementing spatial features and advancing the scalability and accuracy of MIL-based computational pathology.

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