跨尺度注意力融合模型提升癌症生存预测准确率
CrossFusion: A Multi-Scale Cross-Attention Convolutional Fusion Model for Cancer Survival Prediction
- 多尺度切片特征通过交叉注意力融合，捕捉细胞与组织级模式
- 在六种癌症数据集上优于现有最优方法，显著提升生存预测性能
- 适配领域专用主干网络，对病理医生和研究者有实用价值
从全切片图像（WSIs）进行癌症生存预测是计算病理学中的挑战任务，因其图像尺寸大、形状不规则且颗粒度高。这些特性使得难以捕捉从细微细胞异常到复杂组织交互的完整模式，而这些模式对准确预后至关重要。为此，我们提出CrossFusion，一种新型多尺度特征集成框架，从不同放大倍数的图像块中提取并融合信息。通过有效建模尺度特异性模式及其交互关系，CrossFusion生成丰富的特征表示，显著提升生存预测准确性。我们在六个癌症类型（来自公开数据集）上验证了该方法，结果表明其显著优于现有最先进方法。此外，当与领域特定特征提取主干网络结合时，相比通用主干网络，本方法在预后性能上进一步提升。源代码已公开：https://github.com/RustinS/CrossFusion
原文摘要 · Abstract (English)
Cancer survival prediction from whole slide images (WSIs) is a challenging task in computational pathology due to the large size, irregular shape, and high granularity of the WSIs. These characteristics make it difficult to capture the full spectrum of patterns, from subtle cellular abnormalities to complex tissue interactions, which are crucial for accurate prognosis. To address this, we propose CrossFusion, a novel multi-scale feature integration framework that extracts and fuses information from patches across different magnification levels. By effectively modeling both scale-specific patterns and their interactions, CrossFusion generates a rich feature set that enhances survival prediction accuracy. We validate our approach across six cancer types from public datasets, demonstrating significant improvements over existing state-of-the-art methods. Moreover, when coupled with domain-specific feature extraction backbones, our method shows further gains in prognostic performance compared to general-purpose backbones. The source code is available at: https://github.com/RustinS/CrossFusion
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