AI增强的虚拟活检用于低资源环境中的脑肿瘤诊断
AI-Enhanced Virtual Biopsies for Brain Tumor Diagnosis in Low Resource Settings
- Department of Computer Science（计算机科学系）
- Georgia State University（佐治亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出了一种基于轻量级CNN和放射组学特征的虚拟活检系统，用于低资源环境中的脑肿瘤诊断，通过融合策略提升分类性能并增强可解释性。
AI中文摘要：
及时的脑肿瘤诊断在低资源临床环境中仍具挑战性，其中专家神经放射学解释、高端MRI硬件和侵入性活检程序可能受限。尽管深度学习在脑肿瘤分析中取得了强劲表现，但实际应用受到计算需求、跨扫描仪数据偏移和可解释性有限的制约。本文提出了一种原型虚拟活检流程，利用轻量级卷积神经网络（CNN）和互补的放射组学式手工特征，对二维脑MRI图像进行四类分类。基于MobileNetV2的CNN用于分类，而可解释的放射组学分支提取八个特征，捕捉病变形状、强度统计和灰度级共生矩阵（GLCM）纹理描述符。晚期融合策略将CNN嵌入与放射组学特征拼接，并在融合表示上训练随机森林分类器。通过Grad-CAM可视化和放射组学特征重要性分析提供可解释性。在公共Kaggle脑肿瘤MRI数据集上的实验表明，融合方法在验证性能上优于单分支基线，而减少分辨率和加性噪声的鲁棒性测试突显了与低资源成像条件相关的敏感性。该系统被框架为决策支持，而非临床诊断或组织病理学的替代。
英文摘要：
Timely brain tumor diagnosis remains challenging in low-resource clinical environments where expert neuroradiology interpretation, high-end MRI hardware, and invasive biopsy procedures may be limited. Although deep learning has achieved strong performance in brain tumor analysis, real-world adoption is constrained by computational demands, dataset shift across scanners, and limited interpretability. This paper presents a prototype virtual biopsy pipeline for four-class classification of 2D brain MRI images using a lightweight convolutional neural network (CNN) and complementary radiomics-style handcrafted features. A MobileNetV2-based CNN is trained for classification, while an interpretable radiomics branch extracts eight features capturing lesion shape, intensity statistics, and gray-level co-occurrence matrix (GLCM) texture descriptors. A late fusion strategy concatenates CNN embeddings with radiomics features and trains a RandomForest classifier on the fused representation. Explainability is provided via Grad-CAM visualizations and radiomics feature importance analysis. Experiments on a public Kaggle brain tumor MRI dataset show improved validation performance for fusion relative to single-branch baselines, while robustness tests under reduced resolution and additive noise highlight sensitivity relevant to low-resource imaging conditions. The system is framed as decision support and not a substitute for clinical diagnosis or histopathology.
