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.

翻译：摘要：颅内出血（ICH）是一种危及生命的急症，需要及时准确的诊断以实现有效治疗并提高患者生存率。尽管深度学习技术已成为医学图像分析与处理的主流方法，但最常用的监督学习往往需要大量高质量标注数据集，其获取成本高昂，尤其在像素/体素级图像分割任务中。为应对这一挑战并促进ICH治疗决策，本文提出了一种新颖的弱监督ICH分割方法，该方法利用基于类别标签训练的Swin Transformer分类任务。我们的方法通过头向梯度融合自注意力图的分层组合生成精确的图像分割结果。此外，我们开展了不同学习策略的探索性研究，结果表明与完整的ICH亚型分类相比，二分类任务对自注意力图的正面影响更为显著。在平均Dice系数为0.44的条件下，本技术取得了与全监督的经典U-Net和Swin-UNETR模型相当的ICH分割性能，并优于使用GradCAM的同类弱监督方法，充分展示了所提框架在具有挑战性的医学图像分割任务中的卓越潜力。相关代码已开源至https://github.com/HealthX-Lab/HGI-SAM。