Intracranial hemorrhage (ICH) is a critical medical emergency caused by the rupture of cerebral blood vessels, leading to internal bleeding within the skull. Accurate and timely classification of hemorrhage subtypes is essential for effective clinical decision-making. To address this challenge, we propose an advanced pyramid vision transformer (PVT)-based model, leveraging its hierarchical attention mechanisms to capture both local and global spatial dependencies in brain CT scans. Instead of processing all extracted features indiscriminately, A SHAP-based feature selection method is employed to identify the most discriminative components, which are then used as a latent feature space to train a boosting neural network, reducing computational complexity. We introduce an entropy-aware aggregation strategy along with a fuzzy integral operator to fuse information across multiple CT slices, ensuring a more comprehensive and reliable scan-level diagnosis by accounting for inter-slice dependencies. Experimental results show that our PVT-based framework significantly outperforms state-of-the-art deep learning architectures in terms of classification accuracy, precision, and robustness. By combining SHAP-driven feature selection, transformer-based modeling, and an entropy-aware fuzzy integral operator for decision fusion, our method offers a scalable and computationally efficient AI-driven solution for automated ICH subtype classification.

本研究针对颅内出血（ICH）类型分类的挑战，提出了一种基于金字塔视觉变换器（PVT）的模型，旨在提高分类的准确性和可靠性。通过结合SHAP驱动的特征选择和熵感知模糊积分操作，我们的方法显著优于现有深度学习架构，为自动化ICH亚型分类提供了一种可扩展且高效的AI解决方案。

Vision Transformer for Intracranial Hemorrhage Classification in CT
  Scans Using an Entropy-Aware Fuzzy Integral Strategy for Adaptive Scan-Level
  Decision Fusion

基于熵感知模糊积分策略的视觉变换器在CT扫描中颅内出血分类