Détail du document
Identifiant
oai:arXiv.org:2411.04656
Sujet
Computer Science - Computer Vision...Auteur
Yu, Xinlei Elazab, Ahmed Ge, Ruiquan Jin, Hui Jiang, Xinchen Jia, Gangyong Wu, Qing Shi, Qinglei Wang, ChangmiaoCatégorie
Computer Science
Année
2024
Date de référencement
13/11/2024
Mots clés
prognosisRésumé
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
Yu, Xinlei,Elazab, Ahmed,Ge, Ruiquan,Jin, Hui,Jiang, Xinchen,Jia, Gangyong,Wu, Qing,Shi, Qinglei,Wang, Changmiao, 2024, ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism
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