不仅仅是一个热图:通过严格的评估指标提升XAI.
Dost Muhammad1, Malika Bendechache2
1CRT-AI and ADAPT Research Centers, School of Computer Science, University of Galway, Galway, Ireland.
Frontiers in medical technology
|November 13, 2025
概括
新型深度学习框架SpikeNet使用MRI和超声波提供了准确高效的瘤分析. 它提供可解释的AI (XAI) 见解,改善医学成像的临床决策支持.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 计算神经科学是一种神经科学.
背景情况:
- 深度学习 (DL) 模型在使用MRI和超声波的瘤诊断和治疗规划方面表现有前途.
- 然而,高计算要求和DL的有限解释性阻碍了临床采用.
- 现有的可解释AI (XAI) 方法往往会产生碎片化或不整齐的突出地图.
研究的目的:
- 开发SpikeNet,一种混合框架,将CNN和SNN结合起来,用于高效和可解释的瘤分析.
- 介绍XAlign,这是一个衡量人工智能生成的解释与专家注释对齐的指标.
- 评估 SpikeNet 的性能和可解释性与传统的 XAI 方法相比.
主要方法:
- 斯派克网集成了CNN用于空间编码和SNN用于事件驱动处理,具有实时解释的本地突出模块.
- 该XAlign指标通过考虑区域度,边界坚持和分散来评估解释准确性.
- 对TCGA-LGG (MRI) 和BUSI (超声波) 数据集进行了交叉验证,将切片级预测汇总为患者级决策.
主要成果:
- 在TCGA-LGG (97.12%准确率,97.43%F1) 和BUSI (98.23%准确率,98.32%F1) 数据集中,SpikeNet实现了高准确度和F1分数.
- 该框架显示每个图像的延迟很低 (大约为1. 31毫秒) 和高吞吐量 (大约. 32图像/秒) 在单个GPU上.
- 通过XAlign评估的SpikeNet的解释,与Grad-CAM,LIME和SHAP相比显示出更好的对齐.
结论:
- 斯派克网为MRI和超声波提供准确,低延迟和可解释的瘤分析,增强临床决策支持.
- XAlign指标提供了对解释准确性的临床相关评估,使方法之间能够进行可靠的比较.
- 斯派克网和XAlign显示出可靠和高效的临床AI应用的巨大潜力.
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