用混沌扰动增强原型网络，提升稀缺医学图像的分类精度。
Chaos-Enhanced Prototypical Networks for Few-Shot Medical Image Classification
- 引入逻辑混沌模块，在训练中对特征注入可控扰动
- 4类5样本任务下达84.52%准确率，显著优于标准原型网络
- 无需额外计算开销，适合数据稀缺的医疗场景
肿瘤学中标注临床数据稀缺，使得少样本学习（FSL）在辅助诊断中至关重要。我们发现标准原型网络在脑肿瘤扫描中常因形态噪声和类内差异大导致原型不稳定。为此，我们在微调的ResNet-18骨干网络中集成非线性逻辑混沌模块，构建混沌增强原型网络（CE-ProtoNet）。利用逻辑混沌映射的确定性遍历性，在轮次训练中向支持集特征注入受控扰动，相当于对嵌入空间进行“压力测试”。该过程使模型收敛至抗噪表示，且不增加计算开销。在4类5样本脑肿瘤分类任务中，15%混沌注入率能有效稳定高维聚类并降低类内分散。方法取得最高测试准确率84.52%，超越标准原型网络。结果表明，混沌扰动是一种高效、低开销的正则化工具，适用于数据稀缺场景。
原文摘要 · Abstract (English)
The scarcity of labeled clinical data in oncology makes Few-Shot Learning (FSL) a critical framework for Computer Aided Diagnostics, but we observed that standard Prototypical Networks often struggle with the "prototype instability" caused by morphological noise and high intra-class variance in brain tumor scans. Our work attempts to minimize this by integrating a non-linear Logistic Chaos Module into a fine-tuned ResNet-18 backbone creating the Chaos-Enhanced ProtoNet(CE-ProtoNet). Using the deterministic ergodicity of the logistic chaos map we inject controlled perturbations into support features during episodic training-essentially for "stress testing" the embedding space. This process makes the model to converge on noise-invariant representations without increasing computational overhead. Testing this on a 4-way 5-shot brain tumor classification task, we found that a 15% chaotic injection level worked efficiently to stabilize high-dimensional clusters and reduce class dispersion. Our method achieved a peak test accuracy of 84.52%, outperforming standard ProtoNet. Our results suggest the idea of using chaotic perturbation as an efficient, low-overhead regularization tool, for the data-scarce regimes.
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