The classification of gigapixel histopathology images with deep multiple
instance learning models has become a critical task in digital pathology and
precision medicine. In this work, we propose a Transformer-based multiple
instance learning approach that replaces the traditional learned attention
mechanism with a regional, Vision Transformer inspired self-attention
mechanism. We present a method that fuses regional patch information to derive
slide-level predictions and show how this regional aggregation can be stacked
to hierarchically process features on different distance levels. To increase
predictive accuracy, especially for datasets with small, local morphological
features, we introduce a method to focus the image processing on high attention
regions during inference. Our approach is able to significantly improve
performance over the baseline on two histopathology datasets and points towards
promising directions for further research.

我们提出了一种基于 Transformer 的多例学习方法，用区域视觉 Transformer 自注意机制替代传统的学习注意机制，通过融合区域块信息来预测整张幻灯片，并展示了如何在不同距离级别上分层处理特征。为了提高预测准确性，尤其是对于具有小区域形态特征的数据集，我们介绍了一种在推断期间将图像处理集中于高注意区域的方法。我们的方法能够显著提高两个组织病理学数据集上的性能，并指出了进一步研究的有希望的方向。