提出新方法提升病理切片图像分类准确率
SAC-MIL: Spatial-Aware Correlated Multiple Instance Learning for Histopathology Whole Slide Image Classification
- 用坐标编码空间关系，替代传统索引顺序
- 线性复杂度计算全实例关联，速度更快
- 结构简单易部署，适合临床实际应用
我们提出一种用于全切片图像（WSI）分类的自适应空间相关多重实例学习方法（SAC-MIL）。该方法包含位置编码模块，利用切片内实例的坐标信息编码空间关系，而非依赖输入序列中的实例索引，可有效处理训练与测试序列长度不一致的问题。同时引入SAC模块，基于MLP实现线性时间复杂度的全实例相关建模，结构简洁，无需定制CUDA核函数，易于部署。相比基于Transformer的方法，具有更好可扩展性。在CAMELYON-16、TCGA-LUNG和TCGA-BRCA数据集上均达到当前最优性能。代码将在论文接收后开源。
原文摘要 · Abstract (English)
We propose Spatial-Aware Correlated Multiple Instance Learning (SAC-MIL) for performing WSI classification. SAC-MIL consists of a positional encoding module to encode position information and a SAC block to perform full instance correlations. The positional encoding module utilizes the instance coordinates within the slide to encode the spatial relationships instead of the instance index in the input WSI sequence. The positional encoding module can also handle the length extrapolation issue where the training and testing sequences have different lengths. The SAC block is an MLP-based method that performs full instance correlation in linear time complexity with respect to the sequence length. Due to the simple structure of MLP, it is easy to deploy since it does not require custom CUDA kernels, compared to Transformer-based methods for WSI classification. SAC-MIL has achieved state-of-the-art performance on the CAMELYON-16, TCGA-LUNG, and TCGA-BRAC datasets. The code will be released upon acceptance.
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