使用后启发式方法增强脑肿瘤数据集中深度学习模型的可解释性
Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches
AI总结：
本文针对脑肿瘤医学诊断中深度学习模型可解释性不足的问题，提出基于场景特定规则的后启发式方法对 LIME 解释进行后处理，并在公开数据集上验证其能产生更鲁棒、更具体的解释结果。
摘要：
近年来，深度学习模型在医学诊断中的应用已展现出相当可观的效能。然而，一个显著的局限在于决策过程中固有的可解释性不足。本研究通过增强可解释性的鲁棒性来应对这一限制，主要聚焦于改进由 LIME Library 和 LIME 图像解释器生成的解释。这是通过基于场景特定规则的后处理机制实现的。我们利用与脑肿瘤检测相关的公开可访问数据集进行了多项实验。我们提出的后启发式方法在医学诊断背景下展现出显著进展，产生了更鲁棒、更具体的结果。
英文摘要：
The application of deep learning models in medical diagnosis has showcased considerable efficacy in recent years. Nevertheless, a notable limitation involves the inherent lack of explainability during decision-making processes. This study addresses such a constraint, by enhancing the interpretability robustness. The primary focus is directed towards refining the explanations generated by the LIME Library and LIME image explainer. This is achieved throuhg post-processing mechanisms, based on scenario-specific rules. Multiple experiments have been conducted using publicly accessible datasets related to brain tumor detection. Our proposed post-heuristic approach demonstrates significant advancements, yielding more robust and concrete results, in the context of medical diagnosis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
