ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism

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.

;Comment: 6 pages, 2 figures, 3 tables, published to BIBM 2024