Intracerebral hemorrhage (ICH) is the most fatal subtype of stroke and is characterized by a high incidence of disability. Accurate segmentation of the ICH region and prognosis prediction are critically important for developing and refining treatment plans for post-ICH patients. However, existing approaches address these two tasks independently and predominantly focus on imaging data alone, thereby neglecting the intrinsic correlation between the tasks and modalities. This paper introduces a multi-task network, ICH-SCNet, designed for both ICH segmentation and prognosis classification. Specifically, we integrate a SAM-CLIP cross-modal interaction mechanism that combines medical text and segmentation auxiliary information with neuroimaging data to enhance cross-modal feature recognition. Additionally, we develop an effective feature fusion module and a multi-task loss function to improve performance further. Extensive experiments on an ICH dataset reveal that our approach surpasses other state-of-the-art methods. It excels in the overall performance of classification tasks and outperforms competing models in all segmentation task metrics.

翻译：脑出血（ICH）是脑卒中最为致命的亚型，具有高致残率的特点。准确分割脑出血区域并预测预后对于制定和优化脑出血患者的治疗方案至关重要。然而，现有方法通常将这两个任务独立处理，且主要依赖影像数据，从而忽视了任务间与模态间的内在关联。本文提出一个多任务网络ICH-SCNet，旨在同时实现脑出血分割与预后分类。具体而言，我们整合了一种SAM-CLIP跨模态交互机制，将医学文本和分割辅助信息与神经影像数据相结合，以增强跨模态特征识别能力。此外，我们开发了一个有效的特征融合模块和一个多任务损失函数，以进一步提升性能。在脑出血数据集上进行的大量实验表明，我们的方法超越了其他先进方法。它在分类任务的整体性能上表现优异，并且在所有分割任务指标上均优于竞争模型。