网络安全与数据治理2026，Vol.45Issue(4)：59-67,9.DOI:10.19358/j.issn.2097-1788.2026.04.008
基于多目标优化的医学影像可解释性增强研究
Multi-objective optimization for enhanced explainability in medical imaging models
摘要
Abstract
To address the need for reliable interpretability in medical imaging,this study proposes a multi-objective particle swarm optimiza-tion-enhanced explanation framework that improves explanation quality and clinical readability by optimizing the LIME(Local and Model-Ag-nostic Explanations)process.The proposed method incorporates a multi-objective search strategy into the LIME pipeline,enabling an adaptive trade-off between explanatory fidelity and regional sparsity,and producing Pareto-optimal explanation outcomes.Experiments conducted on knee X-ray images from a publicly available knee osteoarthritis dataset using representative convolutional neural networks demonstrate that the method increases fidelity by up to 18%and reduces sparsity by up to 22%,resulting in more focused and stable explanations.These results in-dicate that the proposed framework offers a feasible and effective pathway toward trustworthy AI-driven medical image interpretation.关键词
膝骨关节炎/医学影像可解释性/多目标粒子群优化/LIME/可信赖医疗人工智能Key words
knee osteoarthritis/medical image explainability/multi-objective particle swarm optimization/LIME/trustworthy medical artifi-cial intelligence分类
信息技术与安全科学引用本文复制引用
李海芳,唐超,岳鑫,张强..基于多目标优化的医学影像可解释性增强研究[J].网络安全与数据治理,2026,45(4):59-67,9.基金项目
国家重点研发计划(2024YFA1012700) （2024YFA1012700）
教育部人文社科项目(25YJCZH119) （25YJCZH119）
