用可解释的AI开发一个集体CNN模型,用于胃肠道癌症的分类
Muhammad Muzzammil Auzine1, Maleika Heenaye-Mamode Khan1, Sunilduth Baichoo1
1Department of Software and Information Systems, University of Mauritius, Reduit, Mauritius.
PloS one
|June 25, 2024
概括
可解释的人工智能 (XAI) 通过使人工智能决策透明,提高了癌症检测. 这项研究使用Shapley添加式解释 (SHAP) 和局部可解释模型-不可知解释 (LIME) 来提高胃肠道癌症的AI诊断精度.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 计算病理学计算病理学
背景情况:
- 人工智能辅助的癌症检测系统显示出高效率,但由于解释能力有限,面临采用障碍.
- 医疗从业者需要透明的人工智能决策流程来信任人工智能辅助诊断.
- 可解释的人工智能 (XAI) 通过提供可解释的模型预测来解决"黑子"问题.
研究的目的:
- 探索沙普利增量解释 (SHAP) 和局部可解释模型-不可知解释 (LIME) 用于解释AI模型在癌症检测中的预测.
- 提高在临床环境中使用的人工智能系统的透明度和可信度.
- 在胃肠道癌症检测的背景下研究XAI技术的应用.
主要方法:
- 结合InceptionV3,InceptionResNetV2和VGG16卷积神经网络 (CNN) 的整体模型是使用平均化技术开发的.
- 整体模型是使用Kvasir数据集进行训练的,该数据集包含与胃肠道癌症相关的病理发现.
- 在培训后应用了SHAP和LIME来分析图像分类,并确定影响模型预测的关键特征.
主要成果:
- 整体模型实现了高性能,精度为96.89%,F1得分为96.877%.
- 在SHAP和LIME分析中,成功地确定了对不同胃肠道癌症类别的模型预测作出贡献的决定性特征.
- XAI方法为模型的决策提供了明确的解释,提高了可解释性.
结论:
- 这项研究证明了SHAP和LIME在解释胃肠道癌症检测AI预测方面的成功应用.
- XAI的方法显著提高了人工智能模型的透明度,促进了更大的信任和在临床实践中潜在的采用.
- 这项研究标志着XAI在医疗保健中利用更可靠,更易于理解的AI驱动诊断工具的积极进展.
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