  Intracerebral Hemorrhage (ICH) is the deadliest subtype of stroke,
necessitating timely and accurate prognostic evaluation to reduce mortality and
disability. However, the multi-factorial nature and complexity of ICH make
methods based solely on computed tomography (CT) image features inadequate.
Despite the capacity of cross-modal networks to fuse additional information,
the effective combination of different modal features remains a significant
challenge. In this study, we propose a joint-attention fusion-based 3D
cross-modal network termed ICHPro that simulates the ICH prognosis
interpretation process utilized by neurosurgeons. ICHPro includes a
joint-attention fusion module to fuse features from CT images with demographic
and clinical textual data. To enhance the representation of cross-modal
features, we introduce a joint loss function. ICHPro facilitates the extraction
of richer cross-modal features, thereby improving classification performance.
Upon testing our method using a five-fold cross-validation, we achieved an
accuracy of 89.11%, an F1 score of 0.8767, and an AUC value of 0.9429. These
results outperform those obtained from other advanced methods based on the test
dataset, thereby demonstrating the superior efficacy of ICHPro. The code is
available at our Github: https://github.com/YU-deep/ICH.
