Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images

ABSTRACT Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL based analytical pipelines for WSI-level analysis, the WSIs are often divided into patches and deep features for patches (i.e., patch embeddings) are extracted prior to training to reduce the overall computational cost and cope with the GPUs' limited RAM. Experiments on the SICAPv2 dataset show that our approach outperforms MIL without augmentation and is on par with traditional Figure 1. The feature extractor can either be pre-trained on an auxiliary task [3] or based on self-supervised learning 1. INTRODUCTION Due to the large number of patches per WSI (can be > 10, 000), this step is computationally intensive and is Computational pathology has made significant progress typically only performed once. Then, in the second step, a in recent years with new methods capable of classifying neural network combines the low-dimensional patch embeddings high-dimensional whole-slide images (WSI) of the order of into a slide-level representation used for classification.