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

Title: 基于熵感知模糊积分策略的CT扫描图像中颅内出血分类的视觉Transformer适应性扫描级决策融合

Abstract: 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. Show Chinese abstract

Abstract: 颅内出血（ICH）是由脑血管破裂引起的严重医疗急症，导致颅内出血。准确及时地分类出血亚型对于有效的临床决策至关重要。为了解决这一挑战，我们提出了一种基于先进的金字塔视觉变换器（PVT）的模型，利用其分层注意力机制来捕获脑部CT扫描中的局部和全局空间依赖性。而不是不分青红皂白地处理所有提取的特征，我们采用了一种基于SHAP的特征选择方法来识别最具判别力的成分，然后将其用作潜在特征空间来训练增强神经网络，从而减少计算复杂度。我们引入了熵感知聚合策略以及模糊积分算子，以融合多个CT切片的信息，在考虑切片间依赖关系的情况下，确保更全面和可靠的扫描级诊断。实验结果表明，我们的基于PVT的框架在分类准确性、精确性和鲁棒性方面显著优于最先进的深度学习架构。通过结合SHAP驱动的特征选择、基于变压器的建模以及用于决策融合的熵感知模糊积分算子，我们的方法提供了一种可扩展且计算高效的AI驱动解决方案，用于自动化的ICH亚型分类。