因果推理引导的KAN注意力脑肿瘤分类框架

樊亚文; 王翔; 岳震; 俞晓帆

doi:10.11999/JEIT250865

因果推理引导的KAN注意力脑肿瘤分类框架

doi: 10.11999/JEIT250865 cstr: 32379.14.JEIT250865

樊亚文^{1, 2},
王翔^{1, 2},
岳震^{1, 2},
俞晓帆^{1, 2, ,}

1.
南京邮电大学通信与信息工程学院南京 210003
2.
南京医科大学第一附属医院南京 210006

基金项目: 国家自然科学基金项目(62476139)

详细信息

通讯作者:
　20230053@njupt.edu.cn

中图分类号: TN911.73; TP391.41
计量
- 文章访问数: 15
- HTML全文浏览量: 3
- PDF下载量: 2
- 被引次数: 0
出版历程
- 修回日期: 2026-01-22
- 录用日期: 2026-01-22
- 网络出版日期: 2026-02-11

A Causality-Guided KAN Attention Framework for Brain Tumor Classification

FAN Yawen^{1, 2},
WANG Xiang^{1, 2},
YUE Zhen^{1, 2},
YU Xiaofan^{1, 2
, ,}

1.
National Engineering Research Center of Communications and Networking, Nanjing University of Posts Telecommunications, Nanjing 210003, China
2.
Department of Neurosurgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China

Funds: The National Natural Science Foundation of China(62476139)

摘要

摘要: 脑肿瘤分类是医学影像分析中的关键任务，但现有深度学习方法在应对扫描参数差异、解剖位置偏移等因素时仍面临特征混淆问题，且难以建模肿瘤异质性引发的复杂非线性关系。针对这一挑战，本文提出一种因果推理引导的KAN注意力分类框架。首先，基于CLIP模型进行无监督特征提取，捕捉MRI数据中的高层语义特征；其次，基于K-means聚类设计混淆均衡度指标，筛选混淆因子图像。并设计因果干预机制，显式引入混淆样本，同时提出因果增强的损失函数以优化模型的判别能力；最后，在预训练ResNet主干网中引入KAN注意力模块，强化模型对肿瘤局部坏死区与强化边缘的非线性关联建模能力。实验表明，所提出的方法在脑肿瘤分类任务中优于传统CNN与Transformer模型，验证了其在判别能力和鲁棒性方面的优势。本研究为医学影像的因果推理与高阶非线性建模提供了新的技术路径。
- 医学图像处理 /
- 脑肿瘤分类 /
- 因果推理 /
- 注意力机制
Abstract: Objective In recent years, Convolutional Neural Network (CNN)-based Computer-Aided Diagnosis (CAD) systems have advanced brain tumor classification. However, classification performance remains limited due to feature confusion and inadequate modeling of high-order interactions. To address these challenges, this study proposes an innovative framework that integrates causal feature guidance with a KAN attention mechanism. A novel metric, the Confusion Balance Index (CBI), is introduced to quantify real label distribution within clusters. Furthermore, a causal intervention mechanism is designed to explicitly incorporate confused samples, enhancing the model’s ability to distinguish causal variables from confounding factors. In addition, a KAN attention module based on spline functions is constructed to accurately model high-order feature interactions, thereby strengthening the model’s focus on critical lesion regions and discriminative features. This dual-path optimization approach, combining causal modeling with nonlinear interaction enhancement, improves classification robustness and overcomes the limitations of traditional architectures in capturing complex pathological feature relationships. Methods This study employs a pre-trained CLIP model for feature extraction, leveraging its representation capabilities to obtain semantically rich visual features. Subsequently, based on K-means clustering, the Confusion Balance Index (CBI) is introduced to identify confusing factor images, and a causal intervention mechanism is implemented to explicitly incorporate these confused samples into the training set. Based on this, a causal-enhanced loss function is designed to optimize the model’s ability to discriminate causal variables from confounding factors. Furthermore, to address insufficient high-order feature modeling, a Kolmogorov-Arnold Network (KAN)-based attention mechanism is further integrated. This module, built on spline functions, constructs flexible nonlinear attention representations to finely model high-order feature interactions. By fusing this module with the backbone network, the model achieves enhanced discriminative performance and generalization capabilities. Results and Discussions The proposed method significantly outperforms existing approaches across three datasets. On DS1, the model achieves 99.92% accuracy, 99.98% specificity, and 99.92% precision, surpassing benchmarks such as RanMerFormer (+0.15%) and SAlexNet (+0.23%), with improvements exceeding 2% over traditional CNN methods (95%–97%). Although Swin Transformers achieve 98.08% accuracy, their precision (91.75%) highlights the superior robustness of our method in reducing false detections. On DS2, the model attains 98.86% accuracy and 98.80% precision, outperforming the second-best RanMerFormer. On a more challenging in-house dataset, the model maintains 90.91% accuracy and 95.45% specificity, demonstrating generalization to complex scenarios. Performance gains are attributed to the KAN attention mechanism's high-order feature interaction modeling and the causal reasoning module's confounding factor decoupling. These components enhance focus on critical lesion regions and improve decision-making stability in complex scenarios. Experimental results validate the framework's superiority in brain tumor classification, offering reliable support for clinical precision diagnostics. Conclusions Experimental results substantiate that the proposed framework confers marked improvements in brain tumor classification, with the synergistic interaction between the causal intervention mechanism and the KAN attention module serving as the principal factor driving performance gains. Notably, these enhancements are achieved with negligible increases in model parameters and inference latency, thereby ensuring both efficiency and practicality. This study delineates a novel methodological paradigm for medical image classification and underscores its prospective utility in few-shot learning scenarios and clinical decision support systems.
- Medical image processing /
- Brain tumor classification /
- Causal Inference /
- Attention mechanism

HTML全文

图 1 基于因果推理引导的KAN注意力分类框架图

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图 2 结构因果模型

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图 3 数据集样本示例

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图 4 混淆矩阵

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图 5 Grad-CAM++的热力图样本分析

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图 6 t-SNE可视化结果图

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图 7 聚类簇内真实标签分布

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表 1 数据集信息

肿瘤类型	DS1	DS2	私人
脑膜瘤	1645	708	-
垂体瘤	1757	930	-
胶质瘤	1621	1426	891
DLBCL	-	-	468
转移瘤	-	-	743
无肿瘤	2000	-	-
总数	7023	3064	2102
训练集	5712	2451	1680
测试集	1311	613	422

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表 2 不同模型的测试集结果比较(%)

数据集	方法	Pr	Se	Sp	Acc
DS1	FTVT^[24]	98.71	98.70	-	98.70
	Swin Transformer V2^[25]	98.75	98.51	-	98.97
	NeuroNet19^[26]	99.20	99.20	-	99.30
	ResVit^[27]	98.45	98.61	-	98.47
	InceptionV3^[28]	97.97	96.59	99.98	97.13
	CNN^[29]	97.00	97.00	-	97.00
	DCST+SVM^[30]	97.80	96.62	-	97.71
	Swin Transformers^[13]	91.75	-	-	98.08
	Custom built CNN^[31]	95.00	95.00	-	95.16
	RanMerFormer^[10]	99.76	99.75	99.93	99.77
	SAlexNet^[9]	99.37	99.33	-	99.69
	TinyViT*^[41]	99.43	99.44	99.83	99.47
	LeViT*^[42]	98.92	98.93	99.68	99.01
	Mobilenet-v4*^[43]	97.99	97.98	99.37	98.09
	Ours	99.92	99.92	99.98	99.92
DS2	ARM-Net^[32]	96.46	96.09	-	96.64
	GT-Net^[33]	-	-	-	97.11
	ResVit^[27]	98.54	98.54	-	98.53
	GAN+ConvNet^[34]	95.29	94.91	97.69	95.60
	CNN+SVM^[35]	97.30	97.60	98.97	98.00
	RanMerFormer^[10]	98.87	98.46	99.39	98.86
	Gaussian CNN^[36]	97.07	-	-	97.82
	DACBT^[37]	-	98.09	100	98.56
	Custom built CNN^[11]	96.06	94.43	96.93	96.13
	VGG19+CNN^[38]	98.34	98.60	99.28	98.54
	GoogleNet^[39]	97.20	97.30	98.96	97.10
	TinyViT*^[41]	97.73	98.01	99.02	98.05
	LeViT*^[42]	97.10	97.07	98.68	97.39
	Mobilenet-v4*^[43]	97.45	97.19	98.70	97.56
	Ours	98.80	98.70	99.40	98.86
Ours	VIT*^[40]	80.88	80.85	90.60	81.21
	GoogleNet*^[39]	90.09	90.29	95.10	90.10
	TinyViT*^[41]	88.73	88.33	94.20	88.48
	LeViT*^[42]	80.89	80.94	90.57	81.01
	Mobilenet-v4*^[43]	86.72	86.72	93.35	86.46
	Ours	90.86	90.88	95.45	90.91
“*”表示在统一实验设置下复现得到的结果

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表 3 消融实验结果比较(%)

数据集	方法	Pr	Se	Sp	Acc
DS1	ResNet18	99.84	99.83	99.95	99.85
	ResNet18+Causal	99.92	99.92	99.98	99.92
	ResNet18+KAM	99.92	99.92	99.98	99.92
	ResNet18+Causal+KAM	99.92	99.92	99.98	99.92
DS2	ResNet18	98.29	98.13	99.15	98.37
	ResNet18+Causal	98.37	98.42	99.24	98.53
	ResNet18+KAM	98.41	98.48	99.23	98.53
	ResNet18+Causal+KAM	98.76	98.71	99.41	98.86
Ours	ResNet18	87.28	87.46	93.66	87.27
	ResNet18+Causal	89.58	89.72	94.89	89.70
	ResNet18+KAM	89.61	89.79	94.88	89.70
	ResNet18+Causal+KAM	90.86	90.88	95.45	90.91

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表 4 模型性能分析 (%)

模型	平均Acc	参数量(M)	FLOPs(G)
ResNet18	95.15	11.178	1.8186
ResNet18+CAM	96.02	11.222	1.8190
ResNet+SE	92.05	11.222	1.8240
ResNet+CBAM	95.93	11.267	1.8240
ResNet18+KAM	96.56	11.188	1.8189

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表 5 不同因果权重 $ \alpha $下的分类准确率 (%)

DS2
超参数$ \alpha $	0.0	0.1	0.3	0.5	0.7	0.9
Acc(%)	98.37	98.37	98.70	98.70	98.86	98.37

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表 6 不同数据集中胶质瘤的分类准确率(%)

		测试集
		DS1	DS2	私人	Brats2019
训练集	DS1	100	-	90.97	95.90
	DS2	-	95.07	87.20	94.78
	私人	88.48	87.61	95.53	85.56

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