用于癌症检测的可解释深度学习：人工智能架构和可解释性分析的融合
Interpretable deep learning for gastric cancer detection: a fusion of AI architectures and explainability analysis.
摘要(中文译文)
近年来，胃癌病例数量不断增加，需要准确且及时的检测以改善患者的健康状况。传统的癌症检测技术面临可解释性和精确度方面的问题，因此需要基于可解释人工智能的胃癌检测系统。本研究提出了一种新颖的深度学习（DL）融合方法，通过结合三种深度学习架构，即视觉几何组（VGG16）、残差网络-50（RESNET50）和MobileNetV2来检测胃癌。深度学习模型的融合利用了稳健的特征提取和全局上下文理解能力，这最适合图像数据，从而提高癌症检测系统的准确性。所提出的方法随后采用了可解释人工智能（XAI）技术，即局部可解释模型无关解释（LIME），通过可视化呈现模型决策过程的洞察和透明度。LIME的可视化有助于理解对模型决策有贡献的特定图像区域，这可能有助于临床应用。实验结果表明，融合模型的准确率提高了7%，达到了97.8%的准确率，优于各个独立模型。LIME的使用呈现了图像中导致癌症检测的关键区域。胃癌检测准确率的提升使其在临床应用中具有高度适用性。LIME的使用通过呈现决策解释，确保了模型预测的可信度和可靠性，使其对医疗从业者具有实用价值。本研究有助于开发一种人工智能驱动的、可信赖的癌症检测系统，支持临床决策并改善患者预后。
Abstract
The rise in cases of Gastric Cancer has increased in recent times and demands accurate and timely detection to improve patients' well-being. The traditional cancer detection techniques face issues of explainability and precision posing requirement of interpretable AI based Gastric Cancer detection system. This work proposes a novel deep-learning (DL) fusion approach to detect gastric cancer by combining three DL architectures, namely Visual Geometry Group (VGG16), Residual Networks-50 (RESNET50), and MobileNetV2. The fusion of DL models leverages robust feature extraction and global contextual understanding that is best suited for image data to improve the accuracy of cancer detection systems. The proposed approach then employs the Explainable Artificial Intelligence (XAI) technique, namely Local Interpretable Model-Agnostic Explanations (LIME), to present insights and transparency through visualizations into the model's decision-making process. The visualizations by LIME help understand the specific image section that contributes to the model's decision, which may help in clinical applications. Experimental results show an enhancement in accuracy by 7\% of the fusion model, achieving an accuracy of 97.8\% compared to the individual stand-alone models. The usage of LIME presents the critical regions in the Image leading to cancer detection. The enhanced accuracy of Gastric Cancer detection offers high suitability in clinical applications The usage of LIME ensures trustworthiness and reliability in predictions made by the model by presenting the explanations of the decisions, making it useful for medical practitioners. This research contributes to developing an AI-driven, trustworthy cancer detection system that supports clinical decisions and improves patient outcomes.
如何引用
AMA
Junjie Ma, Fang Yang, Rong Yang, Yuan Li, Yongjing Chen. Interpretable deep learning for gastric cancer detection: a fusion of AI architectures and explainability analysis.. Frontiers in immunology. 2025; doi:10.3389/fimmu.2025.1596085.
APA
Junjie Ma, Fang Yang, Rong Yang, Yuan Li, Yongjing Chen (2025). Interpretable deep learning for gastric cancer detection: a fusion of AI architectures and explainability analysis.. Frontiers in immunology. https://doi.org/10.3389/fimmu.2025.1596085
GB/T 7714
Junjie Ma, Fang Yang, Rong Yang, Yuan Li, Yongjing Chen. Interpretable deep learning for gastric cancer detection: a fusion of AI architectures and explainability analysis.[J]. Frontiers in immunology, 2025 doi:10.3389/fimmu.2025.1596085.
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