动态自适应模糊聚类，实时捕捉白血病细胞变化模式
ADNF-Clustering: An Adaptive and Dynamic Neuro-Fuzzy Clustering for Leukemia Prediction
- 结合CNN特征提取与在线模糊聚类，实时更新聚类中心和不确定性参数
- 在C-NMC数据集上轮廓系数达0.51，优于静态聚类方法
- 无需标签、可适应新数据，适合儿科肿瘤监测系统部署
白血病诊断与监测日益依赖高通量图像数据，但传统聚类方法难以适应细胞形态的动态变化且无法实时量化不确定性。本文提出自适应动态神经模糊聚类（ADNF），一种支持流式处理的框架，融合基于卷积神经网络的特征提取与在线模糊聚类引擎。ADNF通过模糊C均值初始化软划分，利用模糊时间指数（FTI）度量熵演化，持续更新微聚类中心、密度及模糊性参数。拓扑优化阶段采用密度加权合并与熵引导分裂，防止过分割与欠分割。在C-NMC白血病显微镜数据集上，该方法轮廓系数达0.51，显著优于静态基线模型。其自适应不确定性建模与无标签运行特性，具备直接集成至INFANT儿科肿瘤网络的潜力，可为个性化白血病管理提供可扩展、实时更新的支持。
原文摘要 · Abstract (English)
Leukemia diagnosis and monitoring rely increasingly on high-throughput image data, yet conventional clustering methods lack the flexibility to accommodate evolving cellular patterns and quantify uncertainty in real time. We introduce Adaptive and Dynamic Neuro-Fuzzy Clustering, a novel streaming-capable framework that combines Convolutional Neural Network-based feature extraction with an online fuzzy clustering engine. ADNF initializes soft partitions via Fuzzy C-Means, then continuously updates micro-cluster centers, densities, and fuzziness parameters using a Fuzzy Temporal Index (FTI) that measures entropy evolution. A topology refinement stage performs density-weighted merging and entropy-guided splitting to guard against over- and under-segmentation. On the C-NMC leukemia microscopy dataset, our tool achieves a silhouette score of 0.51, demonstrating superior cohesion and separation over static baselines. The method's adaptive uncertainty modeling and label-free operation hold immediate potential for integration within the INFANT pediatric oncology network, enabling scalable, up-to-date support for personalized leukemia management.
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