Weakly Supervised Intracranial Hemorrhage Segmentation Using Hierarchical Combination of Attention Maps from a Swin Transformer

Abstract

Intracranial hemorrhage (ICH) is a potentially life-threatening emergency due to various causes. Rapid and accurate diagnosis of ICH is critical to deliver timely treatments and improve patients’ survival rates. Although deep learning techniques have become the state-of-the-art in medical image processing and analysis, large training datasets with high-quality annotations that are expensive to acquire are often necessary for supervised learning. This is especially true for image segmentation tasks. To facilitate ICH treatment decisions and tackle this issue, we proposed a novel weakly supervised ICH segmentation method utilizing a hierarchical combination of self-attention maps obtained from a Swin transformer, which is trained through an ICH classification task with categorical labels. We developed and validated the proposed technique using two public clinical CT datasets (RSNA 2019 Brain CT hemorrhage & PhysioNet). As an exploratory study, we compared two different learning strategies (binary classification vs. full ICH subtyping) to investigate their impacts on self-attention and our weakly-supervised ICH segmentation method. As the first to perform ICH detection and weakly supervised segmentation with a Swin transformer, our algorithm achieved a Dice score of 0.407$\,\pm \,$0.225 for ICH segmentation while delivering high accuracy in ICH detection (AUC = 0.974).