标题： 基于Hemorica数据集的可解释性脑出血分类的类激活映射方法基准测试 显示英文标题

标题： Benchmarking Class Activation Map Methods for Explainable Brain Hemorrhage Classification on Hemorica Dataset

摘要： 可解释的人工智能（XAI）已成为医学影像研究中的关键组成部分，旨在提高深度学习模型的透明度和临床信任度。 本研究专注于通过类别激活映射（CAM）技术进行脑出血诊断的可解释性。 开发了一个流程，使用九种最先进的CAM算法从分类模型中提取像素级分割和检测注释，应用于多个网络阶段，并在Hemorica数据集上进行定量评估，该数据集独特地提供了切片级标签和高质量的分割掩码。 采用了包括Dice、IoU和像素级重叠在内的指标来评估CAM变体。 结果表明，在EfficientNetV2S的第5阶段实现了最强的定位性能，HiResCAM产生了最高的边界框对齐效果，而AblationCAM在像素级Dice（0.57）和IoU（0.40）方面表现最佳，考虑到模型仅用于分类训练而没有分割监督，这代表了较高的准确性。 据目前所知，这是最早定量比较脑出血检测CAM方法的工作之一，建立了可重复的基准，并强调了XAI驱动流程在临床有意义的AI辅助诊断中的潜力。 显示英文摘要

摘要： Explainable Artificial Intelligence (XAI) has become an essential component of medical imaging research, aiming to increase transparency and clinical trust in deep learning models. This study investigates brain hemorrhage diagnosis with a focus on explainability through Class Activation Mapping (CAM) techniques. A pipeline was developed to extract pixellevel segmentation and detection annotations from classification models using nine state-of-the-art CAM algorithms, applied across multiple network stages, and quantitatively evaluated on the Hemorica dataset, which uniquely provides both slice-level labels and high-quality segmentation masks. Metrics including Dice, IoU, and pixel-wise overlap were employed to benchmark CAM variants. Results show that the strongest localization performance occurred at stage 5 of EfficientNetV2S, with HiResCAM yielding the highest bounding-box alignment and AblationCAM achieving the best pixel-level Dice (0.57) and IoU (0.40), representing strong accuracy given that models were trained solely for classification without segmentation supervision. To the best of current knowledge, this is among the f irst works to quantitatively compare CAM methods for brain hemorrhage detection, establishing a reproducible benchmark and underscoring the potential of XAI-driven pipelines for clinically meaningful AI-assisted diagnosis.