用大模型提升脑瘤分类可解释性，准确率达96%
XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence
- 引入加权边界归一化机制，强化肿瘤边界特征
- 双通道模块结合热力图与大模型文本解释，实现可读推理
- 在三种脑瘤上达到96%准确率，适合医疗AI研发者
深度学习显著推动了脑瘤自动化诊断，但临床应用受限于可解释性与计算开销。传统模型常为不可解释的“黑箱”，难以量化恶性生长中复杂的不规则肿瘤边界。为此，我们提出XMorph框架，用于细粒度分类三种常见脑瘤：胶质瘤、脑膜瘤和垂体瘤。通过信息加权边界归一化（IWBN）机制，突出具有诊断意义的边界区域，并融合非线性混沌与临床验证特征，实现更丰富的形态表征。双通道可解释AI模块结合GradCAM++视觉提示与大模型生成的文本推理，将模型决策转化为临床可理解的解释。该框架分类准确率达96.0%，证明可解释性与高性能可在医疗影像AI系统中共存。XMorph源代码与资料已公开于：https://github.com/ALSER-Lab/XMorph。
原文摘要 · Abstract (English)
Deep learning has significantly advanced automated brain tumor diagnosis, yet clinical adoption remains limited by interpretability and computational constraints. Conventional models often act as opaque ''black boxes'' and fail to quantify the complex, irregular tumor boundaries that characterize malignant growth. To address these challenges, we present XMorph, an explainable and computationally efficient framework for fine-grained classification of three prominent brain tumor types: glioma, meningioma, and pituitary tumors. We propose an Information-Weighted Boundary Normalization (IWBN) mechanism that emphasizes diagnostically relevant boundary regions alongside nonlinear chaotic and clinically validated features, enabling a richer morphological representation of tumor growth. A dual-channel explainable AI module combines GradCAM++ visual cues with LLM-generated textual rationales, translating model reasoning into clinically interpretable insights. The proposed framework achieves a classification accuracy of 96.0%, demonstrating that explainability and high performance can co-exist in AI-based medical imaging systems. The source code and materials for XMorph are all publicly available at: https://github.com/ALSER-Lab/XMorph.
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