  Intracranial hemorrhage (ICH) is a life-threatening medical emergency that
requires timely and accurate diagnosis for effective treatment and improved
patient survival rates. While deep learning techniques have emerged as the
leading approach for medical image analysis and processing, the most commonly
employed supervised learning often requires large, high-quality annotated
datasets that can be costly to obtain, particularly for pixel/voxel-wise image
segmentation. To address this challenge and facilitate ICH treatment decisions,
we introduce a novel weakly supervised method for ICH segmentation, utilizing a
Swin transformer trained on an ICH classification task with categorical labels.
Our approach leverages a hierarchical combination of head-wise gradient-infused
self-attention maps to generate accurate image segmentation. Additionally, we
conducted an exploratory study on different learning strategies and showed that
binary ICH classification has a more positive impact on self-attention maps
compared to full ICH subtyping. With a mean Dice score of 0.44, our technique
achieved similar ICH segmentation performance as the popular U-Net and
Swin-UNETR models with full supervision and outperformed a similar weakly
supervised approach using GradCAM, demonstrating the excellent potential of the
proposed framework in challenging medical image segmentation tasks. Our code is
available at https://github.com/HealthX-Lab/HGI-SAM.
