Whole slide image (WSI) classification is a critical task in computational
pathology. However, the gigapixel-size of such images remains a major challenge
for the current state of deep-learning. Current methods rely on
multiple-instance learning (MIL) models with frozen feature extractors. Given
the the high number of instances in each image, MIL methods have long assumed
independence and permutation-invariance of patches, disregarding the tissue
structure and correlation between patches. Recent works started studying this
correlation between instances but the computational workload of such a high
number of tokens remained a limiting factor. In particular, relative position
of patches remains unaddressed. We propose to apply a straightforward encoding
module, namely a RoFormer layer , relying on memory-efficient exact
self-attention and relative positional encoding. This module can perform full
self-attention with relative position encoding on patches of large and
arbitrary shaped WSIs, solving the need for correlation between instances and
spatial modeling of tissues. We demonstrate that our method outperforms
state-of-the-art MIL models on three commonly used public datasets (TCGA-NSCLC,
BRACS and Camelyon16)) on weakly supervised classification tasks. Code is
available at this https URL

整个切片图像（WSI）分类是计算病理学中的一个关键任务，目前深度学习在处理这种大小为千兆像素的图像方面仍存在重大挑战。我们提出了一种称为 RoFormer 层的简单编码模块，采用了内存高效的准确自注意力和相对位置编码，在大型和任意形状的 WSI 切块上进行全自注意力，解决了实例间相关性和组织的空间建模问题。我们的方法在弱监督分类任务上比最先进的 MIL 模型在三个常用的公共数据集（TCGA-NSCLC，BRACS 和 Camelyon16）上表现出色。